Papers by Koustuv Sinha
Language model acceptability judgements are not always robust to context (2023.acl-long)
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Koustuv Sinha, Jon Gauthier, Aaron Mueller, Kanishka Misra, Keren Fuentes, Roger Levy, Adina Williams
| Challenge: | a recent study found that models prefer acceptable inputs over acceptable ones. |
| Approach: | They find that model judgements are generally robust when placed in randomly sampled linguistic contexts, but unstable when contexts match the test stimuli in syntactic structure. |
| Outcome: | The proposed model performance improves when contexts match syntactic structure, and declines when they are unacceptable. |
Efficient Tool Use with Chain-of-Abstraction Reasoning (2025.coling-main)
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Silin Gao, Jane Dwivedi-Yu, Ping Yu, Xiaoqing Ellen Tan, Ramakanth Pasunuru, Olga Golovneva, Koustuv Sinha, Asli Celikyilmaz, Antoine Bosselut, Tianlu Wang
| Challenge: | Recent large language models have made progress at interpreting and executing instructions. |
| Approach: | They propose a method to decouple general reasoning from specialized knowledge . they propose to use abstract reasoning chains and domain tools to reify each chain . |
| Outcome: | The proposed method outperforms baseline methods on QA and mathematical reasoning domains. |
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text (D19-1)
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| Challenge: | Existing datasets for reading comprehension tasks have been used to test the generalization of natural language understanding systems. |
| Approach: | They propose a diagnostic benchmark suite to clarify key issues related to the robustness and systematicity of NLU systems. |
| Outcome: | The proposed benchmark suite clarifies key issues related to the robustness and systematicity of NLU systems. |
Towards Reproducible Machine Learning Research in Natural Language Processing (2022.acl-tutorials)
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Ana Lucic, Maurits Bleeker, Samarth Bhargav, Jessica Forde, Koustuv Sinha, Jesse Dodge, Sasha Luccioni, Robert Stojnic
| Challenge: | a tutorial on reproducibility in ML addresses the problem of research results that are not reproducible. |
| Approach: | They propose a tutorial to ensure reproducible research in ML with an emphasis on computational linguistics and NLP. |
| Outcome: | The proposed tutorial focuses on computational linguistics and NLP . it provides a framework for using reproducibility as a teaching tool in university-level computer science programs. |
Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little (2021.emnlp-main)
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| Challenge: | masked language models (MLMs) pre-train to model higher-order word co-occurrence statistics . authors suggest that such models have learned to represent syntactic structures prevalent in classical NLP pipelines . purely distributional information largely explains the success of pre-training, authors say . |
| Approach: | They propose to pre-train masked language models on sentences with random shuffled word order and show they still achieve high accuracy after fine-tuning on many downstream tasks. |
| Outcome: | The proposed model performs well according to parametric syntactic probes . the authors argue that the model is not all that different from earlier distributional models . |
Probing Linguistic Systematicity (2020.acl-main)
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| Challenge: | Existing evidence that deep natural language understanding models do not learn systematically is lacking. |
| Approach: | They examine whether deep natural language understanding models exhibit systematicity . they find that network architectures can generalize non-systematically . |
| Outcome: | The proposed model generalizes non-systematically, but is unsatisfactory, the authors argue . they show that the current state-of-the-art models do not generalize systematically . |
The ART of LLM Refinement: Ask, Refine, and Trust (2024.naacl-long)
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Kumar Shridhar, Koustuv Sinha, Andrew Cohen, Tianlu Wang, Ping Yu, Ramakanth Pasunuru, Mrinmaya Sachan, Jason Weston, Asli Celikyilmaz
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations and self-improve? |
| Approach: | They propose a reasoning with a refinement strategy called *ART: Ask, Refine, and Trust* that asks necessary questions to decide when an LLM should refine its output and uses it to affirm or deny trust. |
| Outcome: | The proposed reasoning with a refinement strategy achieves a performance gain of +5 points over baselines on two multistep reasoning tasks. |
A Hierarchical Neural Attention-based Text Classifier (D18-1)
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| Challenge: | Existing hierarchical classification models are unable to handle large corpora and the number of categories increases with increasing corpus. |
| Approach: | They propose to use external knowledge to introduce a hierarchical neural attention-based classifier to help with the classification of documents. |
| Outcome: | The proposed model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability. |
ACUEval: Fine-grained Hallucination Evaluation and Correction for Abstractive Summarization (2024.findings-acl)
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| Challenge: | Recent-proposed evaluation metrics for large language models have a preference-bias . however, such metrics often lack interpretability and only offer a single score . |
| Approach: | They propose a metric that leverages the power of large language models to perform two sub-tasks: decomposing summaries into atomic content units and validating them against the source document. |
| Outcome: | The proposed metric improves faithfulness scores on three summarization evaluation benchmarks by 3% compared to the next-best metric. |
Robustness of Named-Entity Replacements for In-Context Learning (2023.findings-emnlp)
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Saeed Goodarzi, Nikhil Kagita, Dennis Minn, Shufan Wang, Roberto Dessi, Shubham Toshniwal, Adina Williams, Jack Lanchantin, Koustuv Sinha
| Challenge: | Modern large language models perform in-context learning, where query- answer demonstrations are shown before the final query. |
| Approach: | They propose to use in-context learning to prompt queries before they are answered . they find that the choice of demonstrations can affect model performance . |
| Outcome: | The proposed model performance improves on named entity replacements across three reasoning tasks and two popular LLMs. |
Learning an Unreferenced Metric for Online Dialogue Evaluation (2020.acl-main)
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| Challenge: | Existing tools for dialogue evaluation do not generalize to unseen datasets and/or need a human-generated reference response during inference. |
| Approach: | They propose an unreferenced automated dialogue evaluation metric that uses large pre-trained language models to extract latent representations of utterances and leverages the temporal transitions that exist between them. |
| Outcome: | The proposed model achieves higher correlation with human annotations in an online setting, while not requiring true responses for comparison during inference. |
How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contexts. (2022.findings-emnlp)
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| Challenge: | Neural Machine Translation systems are prone to gender biases in their learned representations. |
| Approach: | They propose to use contextual sentences to correct gender bias in Neural Machine Translation models. |
| Outcome: | The proposed method can be used to build better, bias-free translation systems. |
The Curious Case of Absolute Position Embeddings (2022.findings-emnlp)
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| Challenge: | In natural language, it is not absolute position that matters, but relative position . et al., 2017) language models incorporate positional encodings that encode absolute (linear) word order. |
| Approach: | They find that Transformer language models encode word order using positional information . they also find that models that use absolute position embeddings over-rely on positional data . |
| Outcome: | The results raise questions about the efficacy of APEs to model the relativity of position information. |
Sometimes We Want Ungrammatical Translations (2021.findings-emnlp)
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| Challenge: | Recent advances in Neural Machine Translation (NMT) systems focus on improving translation quality and improving robustness to perturbations. |
| Approach: | They propose a way to quantify faithfulness to the original text by focusing on word-order perturbations. |
| Outcome: | The proposed method aims to measure faithfulness and robustness in word-order perturbations without deleting or injecting tokens. |
UnNatural Language Inference (2021.acl-long)
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| Challenge: | Recent studies show that pre-trained NLU models understand human-like syntax . however, these models are word order invariant, causing them to assign gold labels to permutations . |
| Approach: | They propose to measure the severity of this issue by examining the properties of particular permutations that lead models to be word order invariant. |
| Outcome: | The proposed model is word order invariant, but it's not human-like syntax. |