Papers by Inderjeet Nair
Drilling Down into the Discourse Structure with LLMs for Long Document Question Answering (2023.findings-emnlp)
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| Challenge: | Long document question answering requires locating relevant paragraphs within a document to answer a question. |
| Approach: | They propose to exploit the discourse structure commonly found in documents to create a condensed representation of the document, enabling a more comprehensive understanding and analysis of relationships between different parts. |
| Outcome: | The proposed approach retains 99.6% of the best zero-shot approach's performance while processing only 26% of tokens used by the best approach in the information seeking evidence retrieval setup. |
A Neural CRF-based Hierarchical Approach for Linear Text Segmentation (2023.findings-eacl)
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Inderjeet Nair, Aparna Garimella, Balaji Vasan Srinivasan, Natwar Modani, Niyati Chhaya, Srikrishna Karanam, Sumit Shekhar
| Challenge: | Existing methods to segment unformatted text and transcripts explicitly train to predict segment boundaries, but they fail to provide a large annotated dataset. |
| Approach: | They propose a method to generate hierarchical segmentation structures based on Wikipedia annotations by using a neural conditional random field. |
| Outcome: | The proposed method outperforms or achieves competitive performance when compared to previous state-of-the-art algorithms. |
MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense Reasoning (2024.acl-long)
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| Challenge: | Existing approaches to generate a reasoning graph from natural language input suffer from error propagation due to autoregressive nature and single-pass-based decoding. |
| Approach: | They propose a method that uses minimum description length to identify consistent properties among different graph samples generated by large language models. |
| Outcome: | The proposed method outperforms previous approaches for generating reasoning graphs from natural language input using large language models. |
Exploiting Language Characteristics for Legal Domain-Specific Language Model Pretraining (2023.findings-eacl)
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| Challenge: | Pretraining large language models has resulted in tremendous performance improvement for many natural language processing tasks. |
| Approach: | They propose to incorporate pretraining objectives that explicitly exploit domain specific language characteristics into the model. |
| Outcome: | The proposed objectives target token-level feature representation and incorporate sentence level semantics. |
Closing the Loop: Learning to Generate Writing Feedback via Language Model Simulated Student Revisions (2024.emnlp-main)
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| Challenge: | Recent advances in language models (LMs) have made it possible to automatically generate feedback that is actionable and well-aligned with human-specified attributes. |
| Approach: | They propose a tool that PROduces Feedback via learning from LM simulated student revisions and propose to iteratively optimize the feedback generator by directly maximizing the effectiveness of students’ overall revising performance. |
| Outcome: | The proposed approach surpasses baseline methods in effectiveness of improving students’ writing and demonstrates enhanced pedagogical values, even though it was not explicitly trained for this aspect. |