Language models lock in early answers, biasing later responses
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
Researchers have identified a fundamental mechanism by which language models lock themselves into interpretations. When an LLM answers an initial question, it inscribes a 'commitment direction' into its internal state that biases subsequent answers about the same entity, even across unrelated queries. Testing 22 open-weight models reveals this self-coupling is predictable and ablatable, yet the circuits that read these commitments diverge significantly across model scales and training regimes. This finding matters for reliability: models may systematically propagate early errors or biases forward, and instruction tuning amplifies the effect unpredictably. Understanding how to control or neutralize commitment directions could improve consistency and reduce cascading hallucinations in multi-turn reasoning.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
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The paper isolates a specific circuit mechanism (commitment directions) that persists across unrelated queries, showing this isn't just context carryover but a structural bias inscribed into model state. The finding that instruction tuning amplifies the effect unpredictably is the critical qualifier: the standard safety practice may be making the problem worse.
This connects directly to the work on shared relational representations from early November, which showed models maintain low-dimensional structures that generalize across contexts. That paper found relations can flip in sign between models; this paper shows those same structural commitments actively bias downstream outputs in predictable ways. Together they suggest model internals encode persistent geometric commitments that aren't query-specific noise but architectural features. The earlier finding on knowledge editing (the two-hop curse) also matters here: if models fail to bridge concepts after fine-tuning, commitment directions may explain why early answers lock in incorrect entity interpretations that later queries inherit.
If researchers demonstrate that zeroing commitment directions in a held-out test set reduces error propagation in multi-turn reasoning benchmarks (like those used in the confabulation work from late October) without degrading single-turn accuracy, that confirms these circuits are both necessary for consistency and separately controllable. If instruction tuning continues to amplify the effect across new model releases through Q1 2027, that signals the finding isn't an artifact of current training but a structural property requiring explicit mitigation in alignment pipelines.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
Language models share relational structure across queries but flip functional signs
Researchers mapped how language models store and apply relational knowledge across different contexts. Using controlled experiments on GPT-2 Small, Qwen2.5, and Llama-3.2, they found that models maintain a low-dimensional shared representation of relations that generalizes to new entities and persists across unrelated queries. Critically, the functional direction of these relations can flip between models even…
MentionsarXiv
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Written Once, Read Differently How Language Models Own Answers Steer Their Next Ones”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.