Papers by Gurusha Juneja
Task Facet Learning: A Structured Approach To Prompt Optimization (2025.findings-acl)
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| Challenge: | Existing approaches to prompt optimization are limited to learning multiple facets of a task from training examples. |
| Approach: | They propose to optimize a text prompt by considering different facets of a task and including them in the prompt. |
| Outcome: | The proposed algorithm can generate long, complex prompts that existing methods are unable to generate. |
LM2: A Simple Society of Language Models Solves Complex Reasoning (2024.emnlp-main)
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| Challenge: | Existing studies show that providing guidance via decomposing the original question into multiple subproblems elicits more robustness in LLM reasoning. |
| Approach: | They propose a language-based decomposition, solution and verification framework that modularizes the decomposer, solution, and verification into three different language models. |
| Outcome: | The proposed model outperforms existing methods on in- and out-domain reasoning problems, outperforming the best baselines by 8.1% on MATH, 7.71% on JEEBench, and 9.7% on MedQA problems. |
Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning (2023.emnlp-main)
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| Challenge: | Recent attempts at prompt decomposition toward solving complex, multi-step reasoning problems depend on the ability of the LLM to simultaneously decompose and solve the problem. |
| Approach: | They propose a decomposition generator that decomposes complex problems into subproblems that require fewer reasoning steps. |
| Outcome: | The proposed method can produce competitive or even better performance compared to its larger successor, GPT-4. |