Papers by Anirudh Ajith
Adapting Language Models to Compress Contexts (2023.emnlp-main)
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| Challenge: | Transformer-based language models have a finite context window and expensive computational cost of processing long text documents. |
| Approach: | They propose to adapt pre-trained LMs into AutoCompressors to compress text into summary vectors . authors propose to use summary vector to speed up inference over long contexts based on a finite context window . |
| Outcome: | The proposed model can compress long contexts into summary vectors, which are accessible as soft prompts. |
Downstream Trade-offs of a Family of Text Watermarks (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) can generate humanlike responses to a variety of requests like writing emails, translating or summarizing content. |
| Approach: | They evaluate the performance of large language models (LLMs) watermarked using three different strategies over a diverse suite of tasks including those cast as k-class classification (CLS), multiple choice question answering (MCQ), short-form generation (e.g., open-ended question answering) and long-form generator (eg. translation) |
| Outcome: | The proposed models can cause significant drops in their effectiveness across a variety of tasks including CLS, MCQ, short-form generation and translation tasks. |
InstructEval: Systematic Evaluation of Instruction Selection Methods (2024.findings-naacl)
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| Challenge: | In-context learning (ICL) performs tasks by prompting a large language model using an instruction and a small set of annotated examples. |
| Approach: | They develop an ICL evaluation suite to evaluate the performance of popular instruction selection methods. |
| Outcome: | The proposed evaluation suite compares instruction selection methods over five metrics relevant to ICL. |
LitSearch: A Retrieval Benchmark for Scientific Literature Search (2024.emnlp-main)
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| Challenge: | Literature search questions pose significant challenges for modern retrieval systems . a lack of domain expertise and reasoning through lengthy papers is a challenge . |
| Approach: | They propose a retrieval benchmark for literature search queries using inline citations from papers and questions about recently published papers. |
| Outcome: | The proposed retrieval benchmarks outperform state-of-the-art retrieval models and reranking pipelines. |