Papers with decoding techniques
Quantifying Bias from Decoding Techniques in Natural Language Generation (2022.coling-1)
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| Challenge: | Natural language generation (NLG) models can propagate social bias towards particular demography. |
| Approach: | They propose to examine whether bias metrics like toxicity and sentiment are impacted by decoding techniques that use stochastic decoding. |
| Outcome: | The proposed methods reveal the imperative of testing inference time bias and provide evidence on the usefulness of inspecting the entire decoding spectrum. |
The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have been shown to possess impressive capabilities, but they are not problem-free. |
| Approach: | They explore the behavior of large language models when presented with (un)answerable queries. |
| Outcome: | The proposed models encode the answerability of an input query, the authors show . they also show that the first decoded token is a strong indicator . |
Societal Biases in Language Generation: Progress and Challenges (2021.acl-long)
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| Challenge: | Language generation techniques can produce undesirable societal biases that can negatively impact marginalized populations. |
| Approach: | They propose to examine how decoding techniques contribute to biases in language generation . they also conduct experiments to quantify the effects of these techniques . |
| Outcome: | The proposed methods can reduce biases and improve user experience, the authors argue . they also show that the proposed techniques can reduce societal biase . |
Graph-Based Decoding for Task Oriented Semantic Parsing (2021.findings-emnlp)
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| Challenge: | Existing paradigms for semantic parsing are sequence-to-sequence and AMR parsers. |
| Approach: | They propose to formulate parsing as a sequence-to-sequence task using graph-based decoding techniques developed for syntactic parsers. |
| Outcome: | The proposed approach is competitive with sequence decoders on the standard setting and offers significant improvements in data efficiency and data availability. |
Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models (2025.coling-main)
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| Challenge: | Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination – generating content that does not align with realworld facts. |
| Approach: | They propose to extrapolate critical token probabilities beyond the last layer to improve decoding by manipulating the predicted distributions at inference time. |
| Outcome: | The proposed methods surpass state-of-the-art on multiple datasets by large margins. |
AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric Knowledge (2025.naacl-long)
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| Challenge: | Existing contrastive methods that ignore the context of a large language model (LLM) fail to handle instances that vary in their amount of conflict, with static methods over-adjusting when conflict is absent. |
| Approach: | They propose a fine-grained, instance-level approach called AdaCAD which dynamically adjusts the degree of conflict based on the degree. |
| Outcome: | The proposed approach outperforms baselines and improves factuality of summaries by 6.19. |
Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs (2023.emnlp-main)
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| Challenge: | Existing metrics for faithfulness of response are not aligned with human judgments. |
| Approach: | They propose a new metric that utilizes (Conditional) Point-wise Mutual Information (PMI) between the generated response and the source document, conditioned on the dialogue. |
| Outcome: | The proposed metric improves on BEGIN benchmarks and shows that it generates more faithful responses than standard decoding techniques. |