Large Language Models (LLMs) show promise in assisting with Register Transfer Level (RTL) design tasks, including code summarization, documentation, and question answering. However, directly applying LLMs to entire RTL codebases often leads to low accuracy in these tasks. This is primarily because LLMs are less exposed to RTL code during pretraining, limiting their ability to understand RTL-specific semantics and structural dependencies. To overcome this challenge, we propose RTLExplain, which builds project-specific knowledge bases to enhance LLM performance on RTL design tasks. Our method is entirely offline and requires no additional training or fine-tuning. Experiments on code summarization using the generated knowledge bases demonstrate consistent improvements across various medium-to-large RTL projects, even when variable names are obfuscated. Furthermore, we use these knowledge bases to support Retrieval-Augmented Generation (RAG) for question answering tasks. Results show that our enhanced knowledge bases, when combined with RAG, improve question-answering accuracy by 37% compared to naïve prompting and 27% compared to conventional RAG.

Recent advancements in large language models (LLMs) have revolutionized natural language processing (NLP) and expanded their applications across diverse domains. However, despite their impressive capabilities, LLMs have been shown to reflect and perpetuate harmful societal biases, including those based on ethnicity, gender, and religion. A critical and underexplored issue is the reinforcement of caste-based biases, particularly towards India's marginalized caste groups such as Dalits and Shudras. In this paper, we address this gap by proposing DECASTE, a novel, multi-dimensional framework designed to detect and assess both implicit and explicit caste biases in LLMs. Our approach evaluates caste fairness across four dimensions: socio-cultural, economic, educational, and political, using a range of customized prompting strategies. By benchmarking several state-of-the-art LLMs, we reveal that these models systematically reinforce caste biases, with significant disparities observed in the treatment of oppressed versus dominant caste groups. For example, bias scores are notably elevated when comparing Dalits and Shudras with dominant caste groups, reflecting societal prejudices that persist in model outputs. These results expose the subtle yet pervasive caste biases in LLMs and emphasize the need for more comprehensive and inclusive bias evaluation methodologies that assess the potential risks of deploying such models in real-world contexts.

Large language models (LLMs) have shown promise in code summarization, yet their effectiveness for Hardware Description Languages (HDLs) like VHDL and Verilog remains underexplored. We propose RoSum-Mcts, an LLM-guided approach inspired by Monte Carlo Tree Search (MCTS) that refines summaries through structured exploration and reinforcement-driven optimization. Our method integrates both local and global context via a hierarchical candidate expansion mechanism and optimizes summaries using a composite reward function balancing functional correctness (FC), local content adequacy (LCA), and fluency. We evaluate RoSum-Mcts on the VHDL-eval and Verilog-eval datasets, demonstrating its consistent outperformance over baseline methods by leveraging structured bottom-up refinement and reinforcement-based optimization. Ablation studies confirm the necessity of both local and global expansion strategies, as well as the importance of balancing FC and LCA for optimal performance. Furthermore, RoSum-Mcts proves robust against superficial modifications, such as variable renaming, maintaining summary quality where baselines degrade. These results establish RoSum-Mcts as an effective and robust HDL summarization framework, paving the way for further research into reinforcement-enhanced code summarization.

Analog circuit topology synthesis is integral to Electronic Design Automation (EDA), enabling the automated creation of circuit structures tailored to specific design requirements. However, the vast design search space and strict constraint adherence make efficient synthesis challenging. Leveraging the versatility of Large Language Models (LLMs), we propose AUTOCIRCUIT-RL,a novel reinforcement learning (RL)-based framework for automated analog circuit synthesis. The framework operates in two phases: instruction tuning, where an LLM learns to generate circuit topologies from structured prompts encoding design constraints, and RL refinement, which further improves the instruction-tuned model using reward models that evaluate validity, efficiency, and output voltage. The refined model is then used directly to generate topologies that satisfy the design constraints. Empirical results show that AUTOCIRCUIT-RL generates ~12% more valid circuits and improves efficiency by ~14% compared to the best baselines, while reducing duplicate generation rates by ~38%. It achieves over 60% success in synthesizing valid circuits with limited training data, demonstrating strong generalization. These findings highlight the framework's effectiveness in scaling to complex circuits while maintaining efficiency and constraint adherence, marking a significant advancement in AI-driven circuit design.

With the unprecedented advancements in Large Language Models (LLMs), their application domains have expanded to include code generation tasks across various programming languages. While significant progress has been made in enhancing LLMs for popular programming languages, there exists a notable gap in comprehensive evaluation frameworks tailored for Hardware Description Languages (HDLs), particularly VHDL. This paper addresses this gap by introducing a comprehensive evaluation framework designed specifically for assessing LLM performance in VHDL code generation task. We construct a dataset for evaluating LLMs on VHDL code generation task. This dataset is constructed by translating a collection of Verilog evaluation problems to VHDL and aggregating publicly available VHDL problems, resulting in a total of 202 problems. To assess the functional correctness of the generated VHDL code, we utilize a curated set of self-verifying testbenches specifically designed for those aggregated VHDL problem set. We conduct an initial evaluation of different LLMs and their variants, including zero-shot code generation, in-context learning (ICL), and Parameter-efficient fine-tuning (PEFT) methods. Our findings underscore the considerable challenges faced by existing LLMs in VHDL code generation, revealing significant scope for improvement. This study emphasizes the necessity of supervised fine-tuning code generation models specifically for VHDL, offering potential benefits to VHDL designers seeking efficient code generation solutions.

Recently, there has been a growing availability of pre-trained text models on various model repositories. These models greatly reduce the cost of training new models from scratch as they can be fine-tuned for specific tasks or trained on large datasets. However, these datasets may not be publicly accessible due to the privacy, security, or intellectual property issues. In this paper, we aim to develop a lightweight student network that can learn from multiple teacher models without accessing their original training data. Hence, we investigate Data-Free Knowledge Amalgamation (DFKA), a knowledge-transfer task that combines insights from multiple pre-trained teacher models and transfers them effectively to a compact student network. To accomplish this, we propose STRATANET, a modeling framework comprising: (a) a steerable data generator that produces text data tailored to each teacher and (b) an amalgamation module that implements a self-regulative strategy using confidence estimates from the teachers' different layers to selectively integrate their knowledge and train a versatile student. We evaluate our method on three benchmark text classification datasets with varying labels or domains. Empirically, we demonstrate that the student model learned using our STRATANET outperforms several baselines significantly under data-driven and data-free constraints.

In a Model Marketplace, multiple parties may submit pretrained neural networks that accomplish similar tasks. These networks usually have different architectures and are trained on different datasets. It would be significantly beneficial to fuse all these models into a single model with superior performance and lower cost of execution. However, the training parties may be unwilling to share their training data, due to data privacy and confidentiality, and may not agree to participate in a federated learning collaboration. As an alternative, we propose a data-free model fusion framework, based on knowledge distillation, to combine several pretrained models into a superior model without the need for the raw training data. We employ a generative approach to synthesize data for knowledge distillation. The data generator needs to be trained to produce a diverse set of samples that have a similar distribution to that of the training data. Generating samples that cause student-teacher disagreement can expand the coverage of the data distribution, reduce chance of mode collapse and, improve data-free knowledge distillation. However, we found that in a multi-teacher setting, encouraging disagreements between the teachers and the student causes confusion for the generators and deteriorates the results. To tackle this, we introduce Generator Assistants (GA), which keep the generators evolving without causing confusion. Experiments on CIFAR-10, CIFAR-100 and Stanford Dogs datasets show that our method greatly improves the data-free model fusion performance compared to the prior art.

Large Language Models (LLMs) have become widely used across diverse NLP tasks and domains, demonstrating their adaptability and effectiveness. In the realm of Electronic Design Automation (EDA), LLMs show promise for tasks like Register-Transfer Level (RTL) code generation and summarization. However, despite the proliferation of LLMs for general code-related tasks, there's a dearth of research focused on evaluating and refining these models for hardware description languages (HDLs), notably VHDL. In this study, we evaluate the performance of existing code LLMs for VHDL code generation and summarization using various metrics and two datasets - VHDL-Eval and VHDL-Xform. The latter, an in-house dataset, aims to gauge LLMs' understanding of functionally equivalent code. Our findings reveal consistent underperformance of these models across different metrics, underscoring a significant gap in their suitability for this domain. To address this challenge, we propose Chain-of-Descriptions (CoDes), a novel approach to enhance the performance of LLMs for VHDL code generation and summarization tasks. CoDes involves generating a series of intermediate descriptive steps based on: (i) the problem statement for code generation, and (ii) the VHDL code for summarization. These steps are then integrated with the original input prompt (problem statement or code) and provided as input to the LLMs to generate the final output. Our experiments demonstrate that the CoDes approach significantly surpasses the standard prompting strategy across various metrics on both datasets. This method not only improves the quality of VHDL code generation and summarization but also serves as a framework for future research aimed at enhancing code LLMs for VHDL.

Circuit topology generation plays a crucial role in the design of electronic circuits, influencing the fundamental functionality of the circuit. In this paper, we introduce CircuitSynth, a novel approach that harnesses LLMs to facilitate the automated synthesis of valid circuit topologies. With a dataset comprising both valid and invalid circuit configurations, CircuitSynth employs a sophisticated two-phase methodology, comprising Circuit Topology Generation and Circuit Topology Refinement. Experimental results demonstrate the effectiveness of CircuitSynth compared to various fine-tuned LLM variants. Our approach lays the foundation for future research aimed at enhancing circuit efficiency and specifying output voltage, thus enabling the automated generation of circuit topologies with improved performance and adherence to design requirements.

Narrative is a ubiquitous component of human communication. Understanding its structure plays a critical role in a wide variety of applications, ranging from simple comparative analyses to enhanced narrative retrieval, comprehension, or reasoning capabilities. Prior research in narratology has highlighted the importance of studying the links between cognitive and linguistic aspects of narratives for effective comprehension. This interdependence is related to the textual semantics and mental language in narratives, referring to characters' motivations, feelings or emotions, and beliefs. However, this interdependence is hardly explored for modeling narratives. In this work, we propose the task of automatically detecting prominent elements of the narrative structure by analyzing the role of characters' inferred mental state along with linguistic information at the syntactic and semantic levels. We introduce a STORIES dataset of short personal narratives containing manual annotations of key elements of narrative structure, specifically climax and resolution. To this end, we implement a computational model that leverages the protagonist's mental state information obtained from a pre-trained model trained on social commonsense knowledge and integrates their representations with contextual semantic embed-dings using a multi-feature fusion approach. Evaluating against prior zero-shot and supervised baselines, we find that our model is able to achieve significant improvements in the task of identifying climax and resolution.

Email is a widely used tool for business communication, and email marketing has emerged as a cost-effective strategy for enterprises. While previous studies have examined factors affecting email marketing performance, limited research has focused on understanding email response behavior by considering email content and metadata. This study proposes a Prototype-based Multi-view Network (PROMINET) that incorporates semantic and structural information from email data. By utilizing prototype learning, the PROMINET model generates latent exemplars, enabling interpretable email response prediction. The model maps learned semantic and structural exemplars to observed samples in the training data at different levels of granularity, such as document, sentence, or phrase. The approach is evaluated on two real-world email datasets: the Enron corpus and an in-house Email Marketing corpus. Experimental results demonstrate that the PROMINET model outperforms baseline models, achieving a ~3% improvement in F1 score on both datasets. Additionally, the model provides interpretability through prototypes at different granularity levels while maintaining comparable performance to non-interpretable models. The learned prototypes also show potential for generating suggestions to enhance email text editing and improve the likelihood of effective email responses. This research contributes to enhancing sender-receiver communication and customer engagement in email interactions.

Recently, several studies on propaganda detection have involved document and fragment-level analyses of news articles. However, there are significant data and modeling challenges dealing with fine-grained detection of propaganda on social media. In this work, we present TWEETSPIN, a dataset containing tweets that are weakly annotated with different fine-grained propaganda techniques, and propose a neural approach to detect and categorize propaganda tweets across those fine-grained categories. These categories include specific rhetorical and psychological techniques, ranging from leveraging emotions to using logical fallacies. Our model relies on multi-view representations of the input tweet data to (a) extract different aspects of the input text including the context, entities, their relationships, and external knowledge; (b) model their mutual interplay; and (c) effectively speed up the learning process by requiring fewer training examples. Our method allows for representation enrichment leading to better detection and categorization of propaganda on social media. We verify the effectiveness of our proposed method on TWEETSPIN and further probe how the implicit relations between the views impact the performance. Our experiments show that our model is able to outperform several benchmark methods and transfer the knowledge to relatively low-resource news domains.

The ability of humans to symbolically represent social events and situations is crucial for various interactions in everyday life. Several studies in cognitive psychology have established the role of mental state attributions in effectively representing variable aspects of these social events. In the past, NLP research on learning event representations often focuses on construing syntactic and semantic information from language. However, they fail to consider the importance of pragmatic aspects and the need to consistently update new social situational information without forgetting the accumulated experiences. In this work, we propose a representation learning framework to directly address these shortcomings by integrating social commonsense knowledge with recent advancements in the space of lifelong language learning. First, we investigate methods to incorporate pragmatic aspects into our social event embeddings by leveraging social commonsense knowledge. Next, we introduce continual learning strategies that allow for incremental consolidation of new knowledge while retaining and promoting efficient usage of prior knowledge. Experimental results on event similarity, reasoning, and paraphrase detection tasks prove the efficacy of our social event embeddings.

The ability to automatically understand and infer characters’ motivations and emotional states is key to better narrative comprehension. In this work, we propose a Transformer-based architecture, referred to as , to model characters’ motives and emotions from personal narratives. Towards this goal, we incorporate social commonsense knowledge about the mental states of people related to social events and employ dynamic state tracking of entities using an augmented memory module. Our model learns to produce contextual embeddings and explanations of characters’ mental states by integrating external knowledge along with prior narrative context and mental state encodings. We leverage weakly-annotated personal narratives and knowledge data to train our model and demonstrate its effectiveness on publicly available dataset containing annotations for character mental states. Further, we show that the learned mental state embeddings can be applied in downstream tasks such as empathetic response generation.

With growing role of social media in shaping public opinions and beliefs across the world, there has been an increased attention to identify and counter the problem of hate speech on social media. Hate speech on online spaces has serious manifestations, including social polarization and hate crimes. While prior works have proposed automated techniques to detect hate speech online, these techniques primarily fail to look beyond the textual content. Moreover, few attempts have been made to focus on the aspects of interpretability of such models given the social and legal implications of incorrect predictions. In this work, we propose a deep neural multi-modal model that can: (a) detect hate speech by effectively capturing the semantics of the text along with socio-cultural context in which a particular hate expression is made, and (b) provide interpretable insights into decisions of our model. By performing a thorough evaluation of different modeling techniques, we demonstrate that our model is able to outperform the existing state-of-the-art hate speech classification approaches. Finally, we show the importance of social and cultural context features towards unearthing clusters associated with different categories of hate.

Caste as a system of social stratification has created a structured culture of oppression in the Indian subcontinent. The rise of social media has paved the way for the democratization of voices providing space for all groups to express, discuss, debate, and form opinions on critical issues. Unfortunately, it also o↵ers a platform for closet or overtly casteist persons to perpetuate discrimination, spread hatred, and sustain casteism under the veil of creating good social media narratives. The overall goal of our work is to model social media narratives associated with caste-specific news stories. To this end, we first aggregate user-generated social media content (e.g., comments) about various caste-related news stories. Next, we analyze these aggregated contents to extract divergent value judgments representing di↵erent opinions associated with these news stories. Finally, our ongoing research will provide means to infer the value judgments for each user-generated content automatically, track divergent narratives related to the particular news story, and tackle casteist social media posts using counter-narratives generated by leveraging on the inferred value judgments.

The intensification of affective polarization worldwide has raised new questions about how social media platforms might be further fracturing an already-divided public sphere. As opposed to ideological polarization, affective polarization is defined less by divergent policy preferences and more by strong negative emotions towards opposing political groups, and thus arguably poses a formidable threat to rational democratic discourse. We explore if prompting perspective-taking on social media platforms can help enhance empathy between opposing groups as a first step towards reducing affective polarization. Specifically, we deploy a randomized field experiment through a browser extension to 1,611 participants on Twitter, which enables participants to randomly replace their feeds with those belonging to accounts whose political views either agree with or diverge from their own. We find that simply exposing participants to “outgroup” feeds enhances engagement, but not an understanding of why others hold their political views. On the other hand, framing the experience in familiar, empathic terms by prompting participants to recall a disagreement with a friend does not affect engagement, but does increase their ability to understand opposing views. Our findings illustrate how social media platforms might take simple steps that align with business objectives to reduce affective polarization.

Recently, generating adversarial examples has become an important means of measuring robustness of a deep learning model. Adversarial examples help us identify the susceptibilities of the model and further counter those vulnerabilities by applying adversarial training techniques. In natural language domain, small perturbations in the form of misspellings or paraphrases can drastically change the semantics of the text. We propose a reinforcement learning based approach towards generating adversarial examples in black-box settings. We demonstrate that our method is able to fool well-trained models for (a) IMDB sentiment classification task and (b) AG's news corpus news categorization task with significantly high success rates. We find that the adversarial examples generated are semantics-preserving perturbations to the original text.

Stories are a very compelling medium to convey ideas, experiences, social and cultural values. Narrative is a specific manifestation of the story that turns it into knowledge for the audience. In this paper, we propose a machine learning approach to capture the narrative elements in movies by bridging the gap between the low-level data representations and semantic aspects of the visual medium. We present a Memory-Augmented Video Semantic Network, called Video SemNet, to encode the semantic descriptors and learn an embedding for the video. The model employs two main components: (i) a neural semantic learner that learns latent embeddings of semantic descriptors and (ii) a memory module that retains and memorizes specific semantic patterns from the video. We evaluate the video representations obtained from variants of our model on two tasks: (a) genre prediction and (b) IMDB Rating prediction. We demonstrate that our model is able to predict genres and IMDB ratings with a weighted F-1 score of 0.72 and 0.63 respectively. The results are indicative of the representational power of our model and the ability of such representations to measure audience engagement.

Research in building intelligent agents have emphasized the need for understanding characteristic behavior of people. In order to reflect human-like behavior, agents require the capability to comprehend the context, infer individualized persona patterns and incrementally learn from experience. In this paper, we present a model called DAPPER that can learn to embed persona from natural language and alleviate task or domain-specific data sparsity issues related to personas. To this end, we implement a text encoding strategy that leverages a pretrained language model and an external memory to produce domain-adapted persona representations. Further, we evaluate the transferability of these embeddings by simulating low-resource scenarios. Our comparative study demonstrates the capability of our method over other approaches towards learning rich transferable persona embeddings. Empirical evidence suggests that the learnt persona embeddings can be effective in downstream tasks like hate speech detection.