Papers by Sandeep Kumar
MixRevDetect: Towards Detecting AI-Generated Content in Hybrid Peer Reviews. (2025.naacl-short)
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| Challenge: | Existing methods for detecting fully AI-generated peer reviews fail to detect finer-grained AI-generated points within mixed-authorship reviews. |
| Approach: | They propose a method to identify AI-generated points in peer reviews using large language models . their approach achieved an F1 score of 88.86%, significantly outperforming existing methods . |
| Outcome: | The proposed method outperforms existing methods in identifying AI-generated points in peer reviews. |
When Reviews Disagree: Fine-Grained Contradiction Analysis in Scientific Peer Reviews (2026.findings-acl)
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| Challenge: | Existing approaches frame reviewer disagreement as binary contradiction detection over isolated sentence pairs, abstracting away review context and obscuring differences in severity of evaluative conflict. |
| Approach: | They propose a fine-grained formulation of reviewer contradiction analysis that operates over full peer reviews by explicitly identifying contradiction evidence spans and assigning graded disagreement intensity scores. |
| Outcome: | The proposed framework outperforms strong single-agent and generic multi-agend baselines in evidence identification and intensity agreement. |
Multimodal Differential Network for Visual Question Generation (D18-1)
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| Challenge: | Current dialog systems show improvement in visual question answering but this does not translate to improved human-AI dialog. |
| Approach: | They propose to use a Multimodal Differential Network to generate natural questions from images using a multimodal differential network. |
| Outcome: | The proposed approach significantly improves over state-of-the-art benchmarks on the quantitative metrics. |
Longform Multimodal Lay Summarization of Scientific Papers: Towards Automatically Generating Science Blogs from Research Articles (2024.lrec-main)
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| Challenge: | Science blogs and lay-speak are critical to communicating scientific information to the general public and policymakers. |
| Approach: | They propose to use presentation transcripts and slides to generate a scientific blog from a research article in layperson's terms. |
| Outcome: | The proposed approach can generate a blog text and select the most relevant figures to explain a research article in layperson’s terms, essentially a science blog. |
Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays (2022.naacl-main)
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| Challenge: | In automatic essay grading, essay traits are important for scoring the essay holistically . a single-task learning system gives the best results for scoring essays holistically and scoring essay traits. |
| Approach: | They propose a way to score essays using a multi-task learning approach . they compare the MTL-based BiLSTM system to a single-task Learning approach based on LSTMs and BiLStms . |
| Outcome: | The proposed system gives better results for scoring essay holistically and scoring essay traits. |
When Reviewers Lock Horns: Finding Disagreements in Scientific Peer Reviews (2023.emnlp-main)
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| Challenge: | despite being widely accepted standard for validating scholarly research, peer-review process has faced criticism. |
| Approach: | They propose a task of automatically identifying contradictions among reviewers on a given article. |
| Outcome: | The proposed model detects contradictory statements from the review pairs and makes it publicly available for further investigations. |
Leveraging the Cross-Domain & Cross-Linguistic Corpus for Low Resource NMT: A Case Study On Bhili-Hindi-English Parallel Corpus (2025.findings-emnlp)
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| Challenge: | linguistic diversity of India poses significant machine translation challenges, authors say . underrepresented tribal languages like Bhili lack high-quality linguistic resources . |
| Approach: | They introduce a Bhili-Hindi-English Parallel Corpus, the first and largest parallel corpus worldwide . they evaluated a wide range of proprietary and open-source MLLMs on bidirectional translation tasks . |
| Outcome: | The proposed corpus spans critical domains such as education, administration, and news. |
BiMol-Diff: A Unified Diffusion Framework for Molecular Generation and Captioning (2026.acl-long)
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| Challenge: | Autoregressive models struggle with long-range dependencies, while standard diffusion processes apply uniform corruption across positions. |
| Approach: | They propose a framework that bridges molecular structures and natural language . it assigns position-dependent corruption based on token recovery difficulty . |
| Outcome: | The proposed framework improves molecule reconstruction and captioning performance on two datasets. |
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator (C18-1)
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| Challenge: | a novel method for obtaining sentence-level embeddings is proposed . the problem of obtaining a semantic embeddable sentence is at the core of understanding languages . |
| Approach: | They propose a method for obtaining sentence-level embeddings by using a sequential encoder-decoder framework. |
| Outcome: | The proposed method outperforms the state-of-the-art on a sentiment analysis task. |
CourtEval: A Courtroom-Based Multi-Agent Evaluation Framework (2025.findings-acl)
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| Challenge: | Existing automated evaluation metrics like ROUGE and BLEU show low correlation with human judgments. |
| Approach: | They propose a multi-agent evaluation framework that integrates multiple agents . they use ROUGE and BLEU to evaluate natural language models . |
| Outcome: | The proposed evaluation framework outperforms the current state-of-the-art methods in two meta-evaluation benchmarks. |
‘Quis custodiet ipsos custodes?’ Who will watch the watchmen? On Detecting AI-generated peer-reviews (2024.emnlp-main)
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| Challenge: | Recent studies have focused on generic AI-generated text detection or estimating fraction of peer-reviews that can be AI-generated. |
| Approach: | They propose a model that detects whether a peer-review is written by ChatGPT and a reviewer-generated model that generates similar outputs upon re-prompting. |
| Outcome: | The proposed model is more robust, but paraphrasing is more effective. |
Can Large Language Models Unlock Novel Scientific Research Ideas? (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) and ChatGPT have marked a turning point in the integration of Artificial Intelligence (AI) into people’s everyday lives. |
| Approach: | They conduct a human evaluation of the novelty, relevancy, and feasibility of the generated future research ideas. |
| Outcome: | The proposed models generate more diverse ideas than GPT-4, GPT-3.5, and Gemini 1.0. |