UNSPECIFIC is a novel framework that synthesizes constraints common to two similar reference articles to reduce copy-pasting, selectively hardens only trivially satisfied constraints to balance difficulty and naturalness, and evaluates satisfaction on both the generated article and its summary to penalize superficial instruction following.
Abstract
Large language models (LLMs) are increasingly expected to follow long lists of constraints in complex instructions, and synthesizing instructions from a reference document (i.e., back-translation) is a widely used method to measure/enhance LLMs'ability to follow complex instructions. However, this method introduces a critical loophole: the constraint synthesis model copies text from the reference as a very specific constraint and the evaluated LLM trivially satisfies the constraint by copying its text in the response. To address these issues, we propose UNSPECIFIC, a novel framework that synthesizes constraints common to two similar reference articles to reduce copy-pasting, selectively hardens only trivially satisfied constraints to balance difficulty and naturalness, and evaluates satisfaction on both the generated article and its summary to penalize superficial instruction following. Consequently, we built the UNSPECIFIC benchmark on news, story, and blog domains to analyze the copy-pasting behavior of LLMs. Our results show that our synthesized constraints are not only more challenging (e.g., the satisfaction rate of GPT-5 Mini drops from 90% to 78%) and natural (LLM win-rate gap improves by 30%) from a human perspective but also mitigate the copy-pasting. We also find that a large portion of constraints are satisfied superficially (i.e., not satisfied in the core narrative of the article). The code and datasets are released at https://github.com/JeetDSharma/UNSPECIFIC.
Large language models (LLMs) excel at programming, but not when the task fixes the target language: prompted with a grammar rare in their training data, their programs usually break the grammar or fail the given specification. Enumerative synthesizers search the space of syntactically correct programs systematically guided by LLMs; the state of the art guides them by approximating LLM proposals into rule frequencies, which loses where each construct belongs and prunes every rule the proposals miss, exactly when the proposals are wrong. We present Narcissus, a synthesizer that keeps the proposals as syntax trees and scores each expansion of a candidate program in its context: does a proposal with the same surrounding structure continue the same way, and does the expansion rebuild a fragment the proposals repeat? A regularization term keeps every rule reachable, so wrong proposals delay the solution but cannot hide it. Across five domains and two search backends, Narcissus beats static guidance at every budget and consistently outperforms re-prompting the LLM to fix its own proposals; it reaches proposal-like programs an order of magnitude sooner and solves $40\%$ of ARC tasks where the raw proposals solve $13\%$, all without a single LLM call during search.
Tilman Hinnerichs, Sebastijan Dumančić, Neil Yorke-Smith· 0 citations
Natural-language requirements for program synthesis are often incomplete or ambiguous, yet large language models are commonly expected to generate code in a single pass. Prior clarification-based methods address this issue by asking follow-up questions when sampled candidate programs disagree, but fixed clarify-on-disagreement policies can overuse clarification and can also overtrust weak behavioral agreement. We present an adaptive routing framework for LLM-based program synthesis that treats clarification as an inference-time control decision. The framework augments a ClarifyGPT-style pipeline with execution-driven confidence estimation, semanticdifference analysis, and bounded candidate expansion, allowing the system to choose among direct generation, additional evidence gathering, and clarification. We evaluate the framework on MBPP, HumanEval, and extended-test variants using GPT-4.1 mini, Claude Haiku 4.5, and GPT-5.4 mini. Adaptive routing improves pass@1 accuracy by up to 7.60 percentage points over single-pass baselines. Compared with fixed-policy clarification, it preserves accuracy while reducing token usage by up to 57.2% for GPT-4.1 mini, and reallocates computation toward harder cases for Claude Haiku 4.5. These results suggest that clarification is most useful when triggered selectively based on execution evidence and semantic disagreement, even when ambiguity is observed indirectly through candidate behavior rather than through explicitly annotated ambiguous requirements.
Muhammad Ahmed, Edwar Tiu, Niyati Nikunj Kapadia et al.· Annual International Compute...· 0 citations
Three conditions over one byte-identical prompt separate a grammar's two jobs: it fixes where generation stops as well as which tokens may be emitted, and both preregistered language claims fail.
IFHierBench is introduced, a hierarchical instruction-following benchmark of 600 prompts stratified across four constraint-tree depths and 35 distinct constraints, each prompt paired with a deterministic checker that verifies satisfaction at every scope.
2D-RoPE is introduced, which organizes text into a 2D grid rather than a 1D sequence and assigns each token a row ID and a column ID, and suggests that viewing text in 2D can benefit language modeling.
Haodong Wen, Yiran Zhang, Yingfa Chen et al.· 0 citations
In this paper, we define the quantity of prompting complexity: for a fixed instruction-tuned language model, what is the shortest plausible prompt that makes deterministic decoding produce a target text? It is an LM-relative analogue of resource-bounded Kolmogorov complexity: the prompt is a program, the model interface is the interpreter, and information omitted from the prompt is supplied by the model's weights, training distribution, tokenizer, template, and decoding rule. Unlike classical Kolmogorov complexity, this measure is intentionally non-universal. In the finite-context setting it is computable by enumeration, but there is no model-independent invariance theorem; the same text may be cheap for one model and inaccessible or expensive for another. To keep the search space aligned with prompt engineering, we restrict programs to plausible human-readable texts rather than arbitrary token strings. We extend the exact definition to soft prompting complexity for approximate outputs, yielding a lossy notion of model-relative text compression and a formal target for prompt optimization. We also define prompting distance by comparing shortest generating prompts, and behavioral prompting complexity for reaching any output satisfying a specification. Based on these formulations, we define a research agenda for empirically studying which texts and behaviors are accessible from short plausible prompts under a fixed LM interface.