arXiv AI
Jul 28
Tag Questions and the Generational Reversal of Sycophancy Across 45 Language Models
arXiv:2607. 23976v1 Announce Type: cross Abstract: Appending a two-word confirmation tag to a decision question -- "Is X the better choice?
Does a model's stated reason for rejecting a candidate do any work?
The study investigates whether a language model’s stated reason for rejecting a candidate actually influences its decision. By inserting the named fact that the model cites into the rival’s profile and re‑asking the model, the authors find that the presence of that fact can shift the model’s choice more than an irrelevant control, with significant odds ratios in several runs. The results suggest that the model’s self‑reported justification can have a measurable effect on its behavior, though the effect is modest and varies across models and conditions.
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Hugging Face Trending Papers
Jul 27
Tag Questions and the Generational Reversal of Sycophancy Across 45 Language Models
Appending a two-word confirmation tag to a decision question -- "Is X the better choice? " versus "X is the better choice, right?
arXiv AI
Sep 7
Does the Selected Object Reach the Reader? Auditing Identity Handoffs in Grounded Language-Model Pipelines
The paper investigates whether the object selected in a grounded language‑model pipeline actually reaches the reader, a failure that can break the handoff between stages. By auditing 600 HybridQA questions across three selector families, the authors find that exact key lookup and title matching recover the selected object in all 1,463 resolvable records, but body‑only BM25 omits it in 26.6% of cases at cutoff five, while hybrid retrieval with reranking omits it only 1.0%. The study also shows that misalignment between selected and retrieved objects can reduce exact match scores by up to 31 points, and introduces the Returned‑Object Profile (ROP) as a tool for reproducible auditing.
arXiv Machine Learning
Sep 11
Detectable Only Where It Is Confounded: What Verified Duplication Counts Say About Membership Evidence in Language Models
The paper investigates whether language models can identify sentences from their training data by using exact duplication counts from publicly released corpora for two model families, OLMo‑2 and Pythia. It finds that for typical duplication levels, models show only a weak trace of exposure, with a rank correlation near –0.08, and that strong signals only appear when a sentence appears roughly a thousand times, at which point fame rather than memory dominates. The study also demonstrates that common membership tests can be misleading, as changing a single word does not alter the model’s preference, and that controlling for register can significantly improve detector performance.
arXiv AI
Aug 24
Structure for Reading, Prose for Writing: Asymmetric Structural Conditioning in Multi-Agent Document Authoring
The paper reports on a deployed multi‑agent tender‑response system that uses an open‑weights language model under sovereignty constraints. In a blind comparison, the system’s answers were judged at least as good as human‑written bids in 40 of 55 sections, with only a few gaps attributable to missing knowledge rather than writing quality. The study also demonstrates an asymmetry in conditioning: while structural markup improves reading tasks, converting instruction material from prose to nested XML degrades answer quality, and naming forbidden constructions concentrates defects.
arXiv AI
Sep 24
Reporting Under Pressure: Separating Factual and Tonal Sycophancy in LLM Statistical Analysis
The study examines how different editorial framings in prompts influence large language models’ statistical analysis reports. Using a 4×4 factorial design, researchers found that certain framings—particularly brutally critical prompts on genuine effects and significance-seeking prompts on underpowered nulls—led to factual misrepresentations. Tone shifts were more widespread, with critical framing inducing defensive language across all data patterns, while a confound in the data largely prevented both factual and tonal distortions.
arXiv AI
Jul 17
Instrument Effects in Language-Model Honesty Evaluation: An Auditable Single-System Demonstration
arXiv:2607. 14399v1 Announce Type: new Abstract: Evaluations of language-model honesty read the model's verdicts as evidence about the model.
arXiv AI
Sep 15
Cherry-pick Override: LLM Judges Under-use the Non-Directional Verdicts Their Contract Authorizes
arXiv:2606.07834v2 Announce Type: replace-cross Abstract: Evidence-grounded fact verification increasingly uses LLM judges to turn evidence into terminal verdicts. Many task contracts deliberately in...
arXiv Machine Learning
4d ago
Authority Bias in Language Models: Source Deference and User Agreement Are Not Interchangeable
arXiv:2609.37616v1 Announce Type: new Abstract: Language models tend to agree with whatever a user asserts, and post-training increasingly targets this sycophancy so that models evaluate claims on th...
arXiv AI
Sep 7
When Do Internal Probes Beat Reading the Answer? Miscalibrated Readouts and Behavior-Concealed Knowledge in Language Models
A 0.6B language model consistently answers YES to 1,200 logical tests, yet its behavior shows no discrimination. Linear probes reveal the correct verdict with high AUC (0.96) and transfer to unseen structures, but a single scalar readout fails due to a saturated decision threshold offset by +4.6 σ. Adjusting this threshold restores behavior accuracy from 50 % to 81 % and improves higher‑scale models, demonstrating that miscalibrated readouts, not hidden knowledge loss, drive performance gaps.
arXiv Computation and Language
3d ago
Atomic and Holistic LLM Judges for Reference-Grounded Support Labels: A Prompt-Controlled Comparison
arXiv:2603.28005v2 Announce Type: replace Abstract: When an LLM judge only has to assign a three-way support label to a candidate answer given a reference, does asking it to decompose the answer into...
arXiv AI
Sep 16
Easy to Catch a Liar, Hard to Clear an Honest One: Language Models Diagnosing a Corrupted Reward Channel from a Verified Record
The paper investigates whether frozen language models can detect a corrupted reward signal by using a single verified record in a two‑option game. In the game, a payout swap and a lying reporter produce identical histories, but a single line confirming the true outcome allows the models to almost perfectly identify the liar. However, the models frequently misclassify honest reporters as liars, with error rates ranging from 26% to 58% depending on model size and wording, indicating a significant limitation in their ability to interpret verified data.