mlsanityData to 14 Oct 2026

Solving NLP Problems through Human-System Collaboration: A Discussion-based Approach

arXiv 2305.11789 ↗First posted 19 May 2023cs.CLcited by 5

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Abstract (arXiv)

Humans work together to solve common problems by having discussions, explaining, and agreeing or disagreeing with each other. Similarly, if a system can have discussions with humans when solving tasks, it can improve the system's performance and reliability. In previous research on explainability, it has only been possible for the system to make predictions and for humans to ask questions about them rather than having a mutual exchange of opinions. This research aims to create a dataset and computational framework for systems that discuss and refine their predictions through dialogue. Through experiments, we show that the proposed system can have beneficial discussions with humans improving the accuracy by up to 25 points in the natural language inference task.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 51 of its 72 references that are in the map. First posted on arXiv 19 May 2023.

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51 of 72 references.

References · 51

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  6. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  7. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  8. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  9. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  10. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  11. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  12. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
38 more
  1. 2015A large annotated corpus for learning natural language inferencecited by 1,847
  2. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  3. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,319
  4. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  5. 2019Adversarial NLI: A New Benchmark for Natural Language Understandingcited by 807
  6. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 766
  7. 2022Large Language Models Can Self-Improvecited by 664
  8. 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 650
  9. 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 612
  10. 2017Opening the Black Box of Deep Neural Networks via Informationcited by 503
  11. 2018e-SNLI: Natural Language Inference with Natural Language Explanationscited by 468
  12. 2019On Measuring Social Biases in Sentence Encoderscited by 398
  13. 2022Unnatural Instructions: Tuning Language Models with (Almost) No Human Laborcited by 397
  14. 2021Do Prompt-Based Models Really Understand the Meaning of their Prompts?cited by 326
  15. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matterscited by 222
  16. 2015Visualizing and Understanding Neural Models in NLPcited by 200
  17. 2018Multimodal Explanations: Justifying Decisions and Pointing to the Evidencecited by 179
  18. 2020WT5?! Training Text-to-Text Models to Explain their Predictionscited by 148
  19. 2021Reframing Human-AI Collaboration for Generating Free-Text Explanationscited by 125
  20. 2023LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructionscited by 124
  21. 2019Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systemscited by 110
  22. 2021Debiasing Pre-trained Contextualised Embeddingscited by 107
  23. 2020NILE : Natural Language Inference with Faithful Natural Language Explanationscited by 96
  24. 2021Towards Zero-Label Language Learningcited by 76
  25. 2022Correcting Robot Plans with Natural Language Feedbackcited by 69
  26. 2021Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Promptscited by 64
  27. 2019Gender-preserving Debiasing for Pre-trained Word Embeddingscited by 61
  28. 2018WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inferencecited by 60
  29. 2021Unmasking the Mask -- Evaluating Social Biases in Masked Language Modelscited by 60
  30. 2022Gender Bias in Masked Language Models for Multiple Languagescited by 60
  31. 2022Rethinking Explainability as a Dialogue: A Practitioner's Perspectivecited by 52
  32. 2022Training Language Models with Language Feedbackcited by 49
  33. 2022Debiasing isn't enough! -- On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Taskscited by 48
  34. 2022Fixing Model Bugs with Natural Language Patchescited by 33
  35. 2022Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedbackcited by 26
  36. 2021Dictionary-based Debiasing of Pre-trained Word Embeddingscited by 24
  37. 2022Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddingscited by 18
  38. 2022Interpretability for Language Learners Using Example-Based Grammatical Error Correctioncited by 13

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Cited by year

Citing papers per year: 2023: 3; 2024: 2.

5 papers.

  1. 2024Evaluating Gender Bias in Large Language Models via Chain-of-Thought Promptingcited by 50
  2. 2023Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correctioncited by 7
  3. 2024A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finishcited by 6
  4. 2023BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptionscited by 5
  5. 2023SAIE Framework: Support Alone Isn't Enough -- Advancing LLM Training with Adversarial Remarkscited by 4