Jason Weston · page 2
Papers
- Recipes for Safety in Open-domain Chatbots (2020 · 201 citations)
Chatbots trained on large collections of human conversation (here, Reddit-based pre-training) absorb toxic language and biases along with useful conversational skills. - Semantic Frame Identification with Distributed Word Representations (2014 · 154 citations)
The paper tackles frame identification: choosing the correct semantic frame for a given predicate in context, the first step of frame-semantic parsing. - Iterative Reasoning Preference Optimization (2024 · 153 citations)
Iterative preference-optimization methods such as Iterative DPO, Self-Rewarding LLMs and SPIN work well for general instruction following. - Bot-Adversarial Dialogue for Safe Conversational Agents (2021 · 141 citations)
Open-domain chatbots trained on large human conversation corpora can pick up toxic behavior. Most safety work has studied human-written text, not what bots say when users deliberately provoke them. The authors propose Bot-Adversarial Dialogue (BAD), a human-and-model-in-the-loop framework. Crowdworkers chat with… - Self-Alignment with Instruction Backtranslation (2023 · 139 citations)
Instruction-tuning LLMs usually depends on costly human-written demonstrations or on outputs distilled from stronger proprietary models, and high-quality annotation is hard to scale. - Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion (2022 · 132 citations)
Large language models often produce fluent but false statements, and their knowledge stops at the training date. This paper presents SeeKeR (Search-engine→Knowledge→Response), which extends the earlier K2R approach with internet search. One transformer runs three steps in sequence, each marked by special tokens: - Importance of Search and Evaluation Strategies in Neural Dialogue Modeling (2019 · 121 citations)
Neural dialogue research has mostly focused on architectures and training objectives. Decoding, meaning how a response is actually generated from a trained model, has received far less attention. This paper isolates the effect of the search algorithm. The authors keep a single attention-based recurrent… - I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling (2021 · 101 citations)
Open-domain chatbots often contradict themselves, and there has been no reliable way to measure how well models detect such inconsistencies. - Self-Taught Evaluators (2024 · 74 citations)
Training model-based evaluators usually depends on large sets of human preference judgments. These are expensive to collect, especially for expert domains such as coding or math, and they lose value as newer models produce better responses. The Meta FAIR authors propose the Self-Taught Evaluator, which trains an… - Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge (2025 · 64 citations)
Self-Rewarding training lets an LLM improve without human labels by scoring its own outputs. However, it trains only the model's answering skill. Its evaluation skill stays fixed, so gains plateau after a few iterations and the model risks reward hacking. The authors add a third role, a meta-judge, alongside the… - Finding Generalizable Evidence by Learning to Convince Q&A Models (2019 · 43 citations)
The paper asks whether the passage sentences that best support an answer can be found automatically, and whether that evidence is useful beyond the model that produced it. - Jump to better conclusions: SCAN both left and right (2018 · 38 citations)
This BlackboxNLP 2018 paper re-examines SCAN, the command-to-action benchmark from Lake and Baroni (2018) that tests compositional generalization in recurrent sequence-to-sequence models. - Following Length Constraints in Instructions (2025 · 22 citations)
Instruction-tuned LLMs and their evaluators tend to favor longer answers. Many prompts do not say how long the answer should be, which makes evaluation ambiguous and lets training exploit this length bias. Yuan et al. (Meta FAIR / NYU) suggest stating the desired length explicitly in the prompt as a word limit. They…
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