Papers by Vikas Yadav
If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering (2021.naacl-main)
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| Challenge: | et al. : evidence retrieval is highly dependent on partial, incorrect or no supporting knowledge. |
| Approach: | They propose a method that retrieves and reranks evidence facts jointly . they propose to account for links between sentences and coverage with the given query . |
| Outcome: | The proposed approach achieves state-of-the-art evidence retrieval performance on two multi-hop question answering datasets. |
Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework (2025.naacl-short)
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| Challenge: | Graph databases like Neo4j are gaining popularity for handling complex, interconnected data, over traditional relational databases. |
| Approach: | They propose an automated pipeline to generate Cypher queries for Neo4j using LLM-As-Database-Filler, a novel strategy for ensuring Cyphere query correctness. |
| Outcome: | The proposed pipeline generates high quality Cypher data containing 29.8k instances across various domains and queries with varying complexities. |
A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (C18-1)
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| Challenge: | Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. |
| Approach: | They propose to use recurrent neural networks to generate NERs over characters, sub-words and/or word embeddings to improve named entity recognition. |
| Outcome: | The proposed architectures are better than those based on feature engineering and other supervised or semi-supervised learning algorithms. |
Prompting with Phonemes: Enhancing LLMs’ Multilinguality for Non-Latin Script Languages (2025.naacl-long)
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Hoang H Nguyen, Khyati Mahajan, Vikas Yadav, Julian Salazar, Philip S. Yu, Masoud Hashemi, Rishabh Maheshwary
| Challenge: | Multilingual LLMs have achieved remarkable benchmark performance, but continue to underperform on non-Latin script languages. |
| Approach: | They propose to integrate phonemic transcriptions as complementary signals to induce script-invariant representations by integrating phonemic and orthographic transcriptions. |
| Outcome: | The proposed approach improves performance for Latin and non-Latin script languages, with 12.6% performance improvement and 15.1% performance improvement compared to randomized ICL retrieval. |
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection (2024.naacl-long)
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Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, Hongxia Jin
| Challenge: | Instruction-tuned Large Language Models (LLMs) can modulate responses based on human instructions, but they can be maliciously steered to impact society in subtle but persistent ways. |
| Approach: | They propose a backdoor attack setting that allows an attacker to inject a virtual prompt into an LLM to steer it without any explicit injection at its input. |
| Outcome: | The proposed method is able to poison the model's instruction tuning data and show that it is highly effective in steering the model. |
Multi-class Hierarchical Question Classification for Multiple Choice Science Exams (2020.lrec-1)
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Dongfang Xu, Peter Jansen, Jaycie Martin, Zhengnan Xie, Vikas Yadav, Harish Tayyar Madabushi, Oyvind Tafjord, Peter Clark
| Challenge: | Prior work has demonstrated that question classification (QC) can help answer a question more accurately. |
| Approach: | They propose to use a large dataset for question classification (QC) that contains 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains to train a BERT-based model. |
| Outcome: | The proposed model achieves a large (+0.12 MAP) gain while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. |
M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Models (2025.naacl-long)
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| Challenge: | Existing approaches to collect instruction fine-tuning data are limited due to their toxicity, privacy and toxicity concerns. |
| Approach: | They propose to use a two-step taxonomy to transform a small set of human written instructions into complex and challenging conversations. |
| Outcome: | M2Lingual has 175K conversations across 70 languages with a balanced mix of high, low and mid-resourced languages. |
Grammar Search for Multi-Agent Systems (2026.acl-long)
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Mayank Singh, Vikas Yadav, Shiva Krishna Reddy Malay, Shravan Nayak, Sai Rajeswar, Sathwik Tejaswi Madhusudhan, Eduardo Blanco
| Challenge: | Several prior approaches have relied on LLM-based free-form search over the code space. |
| Approach: | They propose a more structured framework that explores the same space through a fixed set of composable components. |
| Outcome: | The proposed framework outperforms existing approaches on most benchmarks across two backbone LLMs and two domains: mathematics and question answering. |
Explicit over Implict: Explicit Diversity Conditions for Effective Question Answer Generation (2024.lrec-main)
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| Challenge: | Recent pretrained and large language model-based QAG methods suffer from redundant generation of QA pairs, affecting downstream QA systems. |
| Approach: | They propose to use explicit diversity conditions to generate diverse question-answer synthetic data by focusing on spatial aspects, question types, and entities. |
| Outcome: | The proposed diversity conditions significantly increase diversity in QA generation over existing diversity techniques. |
Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering (2020.acl-main)
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| Challenge: | Evidence retrieval is a critical stage of question answering (QA) . Several multi-hop QA datasets have been proposed recently . |
| Approach: | They propose an unsupervised method that uses only GloVe embeddings to soft-align questions with justification sentences and an iterative process that reformulates queries focusing on terms that are not covered by existing justifications. |
| Outcome: | The proposed method outperforms all previous methods on the evidence selection task on two datasets: MultiRC and QASC. |
Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models (N19-4)
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Rebecca Sharp, Adarsh Pyarelal, Benjamin Gyori, Keith Alcock, Egoitz Laparra, Marco A. Valenzuela-Escárcega, Ajay Nagesh, Vikas Yadav, John Bachman, Zheng Tang, Heather Lent, Fan Luo, Mithun Paul, Steven Bethard, Kobus Barnard, Clayton Morrison, Mihai Surdeanu
| Challenge: | a paper proposes a method for building probabilistic models of complex phenomena such as food insecurity . currently, these models are hand-built for each new situation and require months to construct . |
| Approach: | They propose an approach that builds executable probabilistic models from raw, free text. |
| Outcome: | The proposed approach builds executable probabilistic models from raw, free text. |
CopySpec: Accelerating LLMs with Speculative Copy-and-Paste (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) suffer from slower inference as context size grows, but CopySpec leverages larger contexts to accelerate inference. |
| Approach: | They propose a technique that speculates that the same tokens will follow repeated sequences in the model’s chat history or context and enables seamless copying without compromising output quality. |
| Outcome: | The proposed technique can generate responses that closely resemble previous outputs or responses that can be verbatim extracted from context without compromising output quality and without requiring additional GPU memory. |
Change Is the Only Constant: Dynamic LLM Slicing based on Layer Redundancy (2024.findings-emnlp)
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| Challenge: | Using dynamic slicing, large language models can be used to reduce computational burden and improve performance. |
| Approach: | They propose a dynamic layer-specific pruning approach that leverages the newly proposed Layer Redundancy score to prune parts of individual layers based on redundancy. |
| Outcome: | The proposed method maintains and enhances model performance over the SliceGPT baseline. |
Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning (2022.coling-1)
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Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil
| Challenge: | Existing neural IR systems rely on lexical matching for query-passage alignment, while masked language models use a dual encoder architecture to encode passages and questions into continuous vector representations. |
| Approach: | They propose to enhance the out-of-domain generalization of Dense Passage Retrieval (DPR) through synthetic data augmentation only in the source domain. |
| Outcome: | The proposed model outperforms existing models in in-domain and zero-shot evaluations on Wikipedia-based datasets. |
Variable Layerwise Quantization: A Simple and Effective Approach to Quantize LLMs (2025.findings-acl)
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Razvan-Gabriel Dumitru, Vikas Yadav, Rishabh Maheshwary, Paul Ioan Clotan, Sathwik Tejaswi Madhusudhan, Mihai Surdeanu
| Challenge: | a meta quantization approach quantizes different layers of a large language model at different bit levels. |
| Approach: | They propose a meta quantization approach that quantizes different layers of a large language model at different bit levels. |
| Outcome: | The proposed method quantizes the most important layers to higher bit precision and less important layers at lower bits. |
Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering (D19-1)
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| Challenge: | Arras et al., 2017) suggest an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) . |
| Approach: | They propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering that maximizes the relevance of the selected sentences, minimizes overlap between selected facts, and maximizes coverage of both question and answer. |
| Outcome: | The proposed strategy improves state-of-the-art supervised QA model on two multi-hop QA datasets: AI2’s Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). |
Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models (2025.coling-main)
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| Challenge: | Abstention Ability (AA) is a critical aspect of Large Language Model (LLM) reliability. |
| Approach: | They propose a black-box evaluation approach and a new dataset, Abstain-QA, to rigorously assess AA across varied question types, domains, and task types. |
| Outcome: | The proposed evaluation process and new dataset, Abstain-QA, are crafted to rigorously assess AA across varied question types, domains, and task types. |
Enhancing Alignment using Curriculum Learning & Ranked Preferences (2024.findings-emnlp)
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| Challenge: | Direct Preference Optimization (DPO) is an effective technique that leverages pairwise preference data to align LLMs to human preferences. |
| Approach: | They propose to use pairwise preference data to create multiple preference pairs for a given prompt. |
| Outcome: | The proposed method outperforms standard DPO on MTbench, Vicuna bench, and WizardLM with a score of 7.43 on the test sets. |
Diverse Multi-tool Aggregation with Large Language Models for Enhanced Math Reasoning (2025.findings-emnlp)
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| Challenge: | Multi-TAG uses multiple tools to solve complex math problems over multiple reasoning steps. |
| Approach: | They propose a tool-based LLM framework that leverages multiple tools to solve math problems. |
| Outcome: | The proposed framework outperforms baselines that use individual tools with the same number of runs and significantly outperformed standard baselines. |
Alignment over Heterogeneous Embeddings for Question Answering (N19-1)
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| Challenge: | Existing approaches for non-factoid question answering are based on heterogeneous embeddings that model text at different levels of abstraction. |
| Approach: | They propose a fast, mostly-unsupervised approach for non-factoid question answering called Alignment over Heterogeneous Embeddings (AHE) it aligns each word in the question and candidate answer with the most similar word in retrieved supporting paragraph and a meta-classifier that learns how much to trust the predictions over each representation. |
| Outcome: | The proposed approach outperforms other supervised approaches on the AI2 Reasoning Challenge dataset and the WikiQA dataset. |
ConciseRL: Conciseness-Guided Reinforcement Learning for Efficient Reasoning Models (2025.findings-emnlp)
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| Challenge: | Large language models excel at complex tasks by breaking down problems into structured reasoning steps. |
| Approach: | They propose a hyperparameter-free conciseness score to guide models toward generating correct reasoning traces. |
| Outcome: | The proposed method reduces token usage by 31x on simple problems while improving accuracy by 7% on the hardest problems. |