Papers by Ananya Singha
TSTR: Target Similarity Tuning Meets the Real World (2023.findings-emnlp)
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| Challenge: | Target similarity tuning (TST) is a method of selecting relevant examples in natural language (NL) to code generation through large language models (LLMs). |
| Approach: | They propose to use sentences from a larger language model to improve similarity between two NL inputs and associated code outputs. |
| Outcome: | The proposed model can be trained on a small number of training examples and is cost-effective. |
TeCoFeS: Text Column Featurization using Semantic Analysis (2025.findings-naacl)
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| Challenge: | Existing methods for topic modeling and feature extraction are based on syntactic features and overlook the semantics. |
| Approach: | They propose a semantic text column featurization problem that extracts a small sample smartly using an LLM to label only the sample and then extends that labeling to the whole column using text embeddings. |
| Outcome: | The proposed approach performs better than baselines and naive use of LLMs. |
MetaReflection: Learning Instructions for Language Agents using Past Reflections (2024.emnlp-main)
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Priyanshu Gupta, Shashank Kirtania, Ananya Singha, Sumit Gulwani, Arjun Radhakrishna, Gustavo Soares, Sherry Shi
| Challenge: | Large Language Models (LLMs) have gained popularity due to their ability to generate human-like text and solve complex tasks. |
| Approach: | They propose an offline reinforcement learning technique that augments a semantic memory based on experiential learnings from past trials. |
| Outcome: | The proposed technique boosts Language agents’ performance by 4 % to 16.82 % over the raw GPT-4 baseline and performs on par with existing state-of-the-art prompt optimization techniques while requiring fewer LLM calls. |