Papers by Hiteshi Sharma
Language Models can be Deductive Solvers (2024.findings-naacl)
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| Challenge: | Recent advances have enabled Large Language Models (LLMs) to potentially exhibit reasoning capabilities, but complex logical reasoning remains a challenge. |
| Approach: | They propose a novel language model that internalizes and emulates the reasoning processes of logical solvers and avoids parsing errors by learning strict adherence to solver syntax and grammar. |
| Outcome: | The proposed model outperforms state-of-the-art solver-augmented language models and few-shot prompting methods on public deductive reasoning benchmarks. |
Enhancing Language Model Alignment: A Confidence-Based Approach to Label Smoothing (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have remarkable capabilities across various domains . Reinforcement Learning with Human Feedback (RLHF) phase is crucial for training . label smoothing is a technique that replaces hard labels with soft labels . |
| Approach: | They propose a method that iteratively updates the label smoothing parameter based on preference labels and model forecasts. |
| Outcome: | The proposed method improves the performance of large language models on state-of-the-art alignment tasks. |