multi-turn-text-to-sql-where-underspecified-question-hides-two-dependent-clarification CLM-0618observationsingle paperReviewed by AI
In multi-turn text-to-SQL where an underspecified question hides two dependent clarification layers, current LLM agents almost never surface both layers (joint identification under 5% for all 12 models tested), and this is not explained by how often they ask: ask rates spanning under 10% to over 60% all produce joint identification below 4%, and second-layer identification stays low (1.2-13.8%) even when the first layer is pre-resolved for them.
Capability: Writing a reusable locator: regex, SQL, XPath
Observed on
TIDE-Bench, 514 anchor SQLs from BIRD, two-layer chains only, chain-then-drift order, LLM user simulator; 12 models including Claude-Sonnet-4.6, GPT-4o, Qwen2.5-72B..
Sources
- Measured on 1,542 constructed samples with a paired factorial design controlling for anchor schema and difficulty; identification is judged by a validated LLM user simulator's router labels, so the measure depends on simulator fidelity. Low absolute numbers across all models leave little room for an artifact-free reading, and the primed protocol isolates layer-two failure from layer-one cascade.
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Related claims
- On text-to-SQL questions that are underspecified with respect to the schema, retrieving clause-level fragments (conditions, joins, calculations, groupings, output specs) from a database's own past successful SQL queries and prepending them to existing few-shot pipelines raises execution accuracy across four different pipelines, but the gain saturates and can reverse once too many candidates are retrieved, because high recall with low precision lets the generator pick a plausible-but-wrong pattern.Writing a reusable locator: regex, SQL, XPath · reviewed by AI
- On factual QA (817-item TruthfulQA multiple-choice, plus a 300-item Natural Questions short-answer set), a prompt-only three-stage gate in which the model lists the information units a question needs, scores its support for each, and answers only when the mean score clears a threshold lowers the unconditional wrong-answer rate and raises accuracy on answered items for all eleven models tested, but most of the abstentions it produces fall on questions plain chain-of-thought answered correctly (about 69% at the primary operating point), so the gain is bought with lost coverage rather than with selective self-knowledge.Saying it does not know · reviewed by AI
- When an LLM judge is given a prior evaluation score as context metadata on a revision, its rubric scores shift toward that uninformative prior — seven of eight tested models (API and open-weight, 0-5 scale, 20 fixed texts) show a negative total shift, and on a categorical compliance task the metadata blocks 48% of error corrections and flips about 10% of previously correct judgments to the anchored wrong label.Judging another's work as well as a careful reviewer would · reviewed by AI
- For small instruction-tuned models (3-4B) answering closed-form true/false questions under a stated user opinion, scoring each sampled response by how much more common its answer is than the group itself predicted, and using that score as the GRPO reward, reduces answer flips under user pressure and raises accuracy without any labels, roughly matching label-supervised desycophancy fine-tuning at higher compute cost.Telling the user what they want to hear · reviewed by AI
- On text-to-SQL benchmarks, aggregate accuracy hides a steep difficulty gradient: pre-trained-language-model-based parsers resolved roughly 80% of Spider cases overall but only about 50% of its extra-hard cases, and as natural-language understanding improved the residual errors shifted toward database-side problems — large schemas, ambiguous columns, dirty cell values — rather than language parsing.Writing a reusable locator: regex, SQL, XPath · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: A model that asks for clarification often and, with the first ambiguity layer pre-resolved, identifies the dependent second layer at a high rate (e.g. above 50%), showing the limit is clarification policy rather than identification.