Jul 2026· Annual International ACM SIGIR Conference on Research and Development in Information Retrieval· 0 citations· 54 references
Computer Science
TL;DR
This framework proposes a context-editing framework that performs selective abstraction over entities that appear in both the context and the question, establishing symbolic abstraction as a highly cost-efficient solution for ensuring context fidelity in LLMs.
Abstract
Large language models (LLMs) frequently prioritize parametric world knowledge over provided context -- a failure mode that is particularly catastrophic in enterprise or counterfactual settings where local facts contradict web-scale training data. While modern reasoning models improve general response quality, they fail to resolve these underlying prior knowledge biases even when generating a high volume of costly thinking tokens. We propose a context-editing framework that addresses this by performing selective abstraction over entities that appear in both the context and the question. Our approach replaces these overlapping entities with typed, indexed identifiers (e.g., Paris with City łangle1\rangle), suppressing spurious lexical associations while preserving the relational structure required for grounded reasoning. This mechanism is decoupled into an offline preprocessing stage and a lightweight inference-time substitution, requiring no model retraining. Experiments across counterfactual and multi-hop knowledge-conflict benchmarks demonstrate consistent accuracy gains across many model families, open- and closed-sourced, especially for small to medium sized LLMs (0.5B to 18B). Notably, our framework yields up to a 25% improvement over state-of-the-art instruct models and 13% over reasoning models, establishing symbolic abstraction as a highly cost-efficient solution for ensuring context fidelity in LLMs.
A new benchmark, KIFI, is designed, which comprises 1032 carefully selected instances from the TRUE and ScreenEval datasets, with key information annotated, and it is shown that LLMs frequently fail to use the appropriate information to make correct decisions.
Xindi Guo, Zhen Xie, Patrick H. Chen· Annual International ACM SIG...· 0 citations
The results demonstrate that suppressing entity-linked supervision during pretraining induces a shift in epistemic behavior: KLLMs rely less on parametric knowledge and more on external evidence, leading to improved reliability under realistic conditions, suggesting that pretraining-time control over knowledge acquisition can complement retrieval-augmented and tool-based systems by providing a more evidence-sensitive base model.
Roi Cohen, Yvan Carr'e, Nick Lechtenborger et al.· 0 citations
Lite-CoNER is proposed, a lightweight NER framework that achieves an effective balance between recognition accuracy and inference efficiency and provides a transparent view of the decision-making process, proving that lightweight models can effectively inherit complex logic through structured distillation.
Yang Wang, Lushuang Gao· International Conference on...· 0 citations
HPSE is proposed, which builds a hybrid rollout that steps in to place missing facts onto the student's own trajectory precisely where its coverage fails, while staying on-policy elsewhere.
Tianci Liu, Zihan Dong, Tianchun Li et al.· 0 citations
Model editing keeps large language models (LLMs) up to date without retraining, but temporal facts expose a limitation of the prevailing locate-and-edit paradigm: an update is not always a replacement. When a fact changes, the new answer should become current while the old answer may remain correct in historical time contexts. Building on this insight, we use causal tracing to show that LLMs already support this distinction via a two-stage internal computation: early MLP layers retrieve a time-agnostic subject representation, and later layers modulate it with temporal context to yield the time-correct answer. Motivated by this finding, we introduce PRISM Edit, which optimizes a single polysemous representation across temporal contexts and leverages the model's inherent modulation pathway to route it to temporally correct predictions without requiring any architectural modification. We evaluate on TimeConflict, a newly introduced temporal editing benchmark, and on temporally augmented CounterFact. PRISM Edit improves multiple core metrics over the best baseline, most notably +23.3 Temporal Consistency (TC) and +33.7 Current Relative-time Score (CRS) on LLaMA-3, while being more than 2x faster. Code and data are publicly available at https://github.com/CheerCHuang/PRISM-Edit.
Chen Huang, Qirui Zheng, Ruiqin Zheng et al.· 1 citation
Large language models (LLMs) increasingly handle in-context learning (ICL) tasks where a long, novel context defines the rules, knowledge, and output schema for a series of questions. On benchmarks that grade against every detail of the context, even strong open-weights models pass only 12-16% of tasks: a single overlooked rule fails the whole response. We argue this brittleness is structural: the dominant"read-and-reason"paradigm asks the model to extract, plan, generate, and self-verify in one forward pass. We therefore ask whether explicit context compilation can fix it, how it compares to existing long-context strategies (gist retrieval, multi-agent self-play), and where the resulting harness benefit holds across task structure and model scale. We propose the Context Compilation Architecture (CCA), whose central novelty is a typed intermediate representation (IR) with fixed slots (rules.{must_do, must_not, conditional}, output_spec, available_tools, data_profile) into which any prose context is compiled once; executable verifiers and a violation-gated correction loop follow as downstream consequences. On CL-bench (1,899 tasks across 4 open base models), CCA outperforms vanilla prompting and two long-context baselines (ReadAgent-P, Ctx2Skill) on every base model, lifting Kimi K2.5 from 15.4% to 21.4% with gains concentrated on rule-dense sub-categories. Code and cached completions are available at https://github.com/TonyQJH/cca-emnlp2026.
Jin-Hu Qi, Minda Hu, Wen-Tao Zhang et al.· 0 citations
