Papers by Himabindu Lakkaraju
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent studies suggest using large language models to make tabular classifications . however, LLMs have been shown to exhibit harmful social biases based on stereotypes and inequalities present in society. |
| Approach: | They propose to use large language models to make tabular classifications . they show that LLMs inherit biases from their training data . |
| Outcome: | The proposed models exhibit harmful biases that reflect stereotypes and inequalities in society. |
How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior (2026.acl-long)
Copied to clipboard
Zidi Xiong, Yuping Lin, Wenya Xie, Pengfei He, Zirui Liu, Jiliang Tang, Himabindu Lakkaraju, Zhen Xiang
| Challenge: | In practice, memory designs vary widely across agents due to their diverse objectives and functionalities. |
| Approach: | They conduct an empirical study on how memory management choices impact the LLM agents’ behavior, especially their long-term performance. |
| Outcome: | The proposed methods show that LLM agents display an experience-following property, which results in highly similar agent outputs. |
Evaluating Adversarial Robustness of Concept Representations in Sparse Autoencoders (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing evaluations of SAEs focus on metrics such as reconstruction-sparsity tradeoff, human (auto-)interpretability, and feature disentanglement, but they neglect robustness of concept representations to input perturbations. |
| Approach: | They propose an unsupervised approach to map LLM embeddings to sparse interpretable concept embeddables via dictionary learning. |
| Outcome: | The proposed framework shows that sparse autoencoders can manipulate concept-based interpretations without denoising or postprocessing. |
Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Recent studies have highlighted weak-to-strong generalization, where a strong model trained only on a weak model’s labels surpasses the weak model in task performance. |
| Approach: | They propose two fundamental fine-tuning strategies that leverage trustworthiness regularization during the fine-uning of the weak model and the weak-to-strong transfer to improve trustworthy. |
| Outcome: | The proposed models show that they can generalize robustness, fairness, and privacy better when trained on weak models than models trained on strong models. |
On the Impact of Fine-Tuning on Chain-of-Thought Reasoning (2025.naacl-long)
Copied to clipboard
| Challenge: | Large language models have emerged as powerful tools for general intelligence, showcasing advanced natural language processing capabilities. |
| Approach: | They propose to use supervised fine-tuning and Quantized Low-Rank Adapters to improve LLMs' task-specific performance to address privacy and safety risks. |
| Outcome: | The proposed model improves the accuracy of the chain-of-thought reasonings across four datasets and demonstrates that the faithfulness of CoT reasoning decreases. |
A Study on the Calibration of In-context Learning (2024.naacl-long)
Copied to clipboard
Hanlin Zhang, YiFan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric Xing, Himabindu Lakkaraju, Sham Kakade
| Challenge: | Prior research has demonstrated improvements in the calibration of language models (LMs) in-context learning is a popular method for adapting static LMs to safety-critical domains. |
| Approach: | They use in-context learning to adapt static language models through tailored prompts to a wide range of tasks and find that miscalibration occurs in low-shot settings. |
| Outcome: | The proposed calibrations show that models exhibit increased miscalibration before achieving better calibration in low-shot settings. |