LLM-based fact-checking verifies complex claims through decomposition, retrieval, and reasoning aggregation. Existing methods expand verification inputs to cover more factual conditions, but added fragments can be redundant or misleading, compete with decisive evidence, and bias predictions toward surface relevance. Debiasing methods mitigate this bias by estimating shortcut effects from claim-side cues or biased signals in constructed inputs. However, because they calibrate verifier predictions after input construction, they are not designed to repair missing or noisy verification states. Thus, complex fact-checking should balance factual coverage and noise control during verification-state construction. We propose ERGA, an Explorer–Refiner Game with Backdoor-Adjusted Verdict Aggregation. ERGA formulates verification as editable state construction, initializing the state from decomposed verification units, retrieved evidence, and local analyses. It then runs an adversarial Explorer–Refiner game over this state: the Explorer strengthens under-covered factual signals, while the Refiner merges redundancy and separates weak or misleading fragments. Through this game interaction, ERGA obtains a refined state with a better coverage–noise balance and a removed fragment record. For verdict aggregation, ERGA uses the removed-fragment record to condition verifier predictions and aggregates them in a backdoor-adjusted form to reduce residual shortcut effects. Experiments on HoVer and FEVEROUS-S show that ERGA outperforms strong baselines.

Emotional support conversation (ESC) aims to alleviate users’ psychological stress. Selecting the appropriate strategy is crucial for effective emotional support. Current strategy planner-based methods prioritize immediate responses while neglecting users’ future reactions. Some studies retrieve historical examples with similar emotions to the current utterance, then anticipating future emotions based on next-turn emotions of historical examples. However, their retrievals focus on the current emotion (i.e. a single-turn emotion state), while they ignore the evolution of user’s emotion before the current state. We argue that retrievals considering the whole emotional trajectories enables models to capture the dynamic emotional needs, thereby enhancing the anticipation of future emotions. To this end, we propose Markov-driven emotion anticipation framework with emotion trajectory-aware retrieval for LLM-based ESC, which anticipates future emotion states to guide strategy planning and achieve sustained emotional support. First, we construct a dynamic emotion memory and perform hierarchical retrieval that combines semantic matching and emotion trajectory alignment. Then, we model emotional transitions as Markov chains, leveraging trajectory-aware retrieval to estimate future emotion. Finally, we use the anticipated emotion to steer LLMs in generating candidate strategies and introduce active online learning to optimize the planner, boosting its robustness on diverse users. Experiments on two datasets with two models shows that our method excels all baselines.

Text understanding application often suffers from domain shifts. To handle testing domains, domain adaptation (DA) is trained to adapt to a fixed and observed testing domain; a more challenging paradigm, test-time adaptation (TTA), cannot access the testing domain during training and online adapts to the testing samples during testing, where the samples are from a fixed domain. We aim to explore a more practical and underexplored scenario, continual test-time adaptation (CTTA) for text understanding, which involves a sequence of testing (unobserved) domains in testing. Current CTTA methods struggle in reducing error accumulation over domains and enhancing generalization to handle unobserved domains: 1) Noise-filtering reduces accumulated errors but discards useful information, and 2) accumulating historical domains enhances generalization, but it is hard to achieve adaptive accumulation. In this paper, we propose a CTTA-T (continual test-time adaptation for text understanding) framework adaptable to evolving target domains: CTTA-T adopts a teacher-student framework, where the teacher is equipped with domain awareness and generalization for evolving domains. To improve teacher predictions, we propose a refine-then-filter based on dropout-driven consistency, which calibrates predictions and removes unreliable guidance. For the adaptation–generalization trade-off, we construct a domain-aware teacher by dynamically accumulating cross-domain semantics via incremental PCA, which continuously tracks domain shifts. Experiments show CTTA-T excels baselines.

LLMs with in-context learning (ICL) obtain remarkable performance but are sensitive to the quality of ICL examples. Prior works on ICL example selection explored unsupervised heuristic methods and supervised LLM-based methods, but they typically focus on the selection of individual examples and ignore correlations among examples. Researchers use the determinantal point process (DPP) to model negative correlations among examples to select diverse examples. However, the DPP fails to model positive correlations among examples, while ICL still requires the positive correlations of examples to ensure the consistency of examples, which provides a clear instruction for LLMs. In this paper, we propose an ICL example selection method based on the nonsymmetric determinantal point process (NDPP) to capture positive and negative correlations, considering both the diversity and the relevance among ICL examples. Specifically, we optimize NDPP via kernel decomposition-based MLE to fit a constructed pseudo-labeled dataset, where we also propose a low-rank decomposition to reduce the computational cost. Further, we perform query-aware kernel adaptation on our NDPP to customize the input query, and we select examples via a MAP inference based on the adapted NDPP. Experimental results show our model outperforms strong baselines in ICL example selection.