Papers with -shot demonstrations
Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context Learning (2026.eacl-long)
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| Challenge: | In-Context Learning (ICL) is a key feature for Large Language Models (LLMs) but it faces challenges when dealing with increasing numbers of examples due to performance degradation and quadratic computational costs. |
| Approach: | They propose a Logit Arithmetic Reweighting Approach that uses logit-based ensembling of multiple demonstrations to enhance ICL. |
| Outcome: | The proposed framework outperforms baseline methods in accuracy and memory efficiency. |
On the Additive Compositionality of Task Vectors in Vision–Language Models (2026.eacl-short)
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| Challenge: | In-context learning (ICL) in large language models (LLMs) has been shown to operate through task vectors, but its extension to vision-language models (VLMs) remains underexplored. |
| Approach: | They construct visual reasoning tasks with clearly defined subtasks and extract task vectors from few-shot demonstrations. |
| Outcome: | The proposed model can be extended to vision-language models (VLMs) by adding the vectors of its constituent subtasks. |
Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions (2024.findings-naacl)
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| Challenge: | Large Language Models have demonstrated impressive capabilities in various NLP tasks, but previous studies have shown they are sensitive to prompt wording and few-shot demonstrations and their order. |
| Approach: | They focus on LLMs robustness on multiple-choice questions . they find a performance gap of 13% to 85% when options are reordered . |
| Outcome: | The proposed model outperforms supervised models on multiple choice questions even outperforming humans. |
INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning (2024.acl-long)
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Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks, but their application to information retrieval tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language. |
| Approach: | They propose to use instruction tuning to enhance LLMs' proficiency in IR tasks by combining a dataset with manually written templates to analyze the effects of instruction design, template diversity, few-shot demonstrations, and the volume of instructions. |
| Outcome: | The proposed model can be used to perform query understanding, document understanding, and query-document relationship understanding tasks. |
Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods that rely on limited demos and out-of-demonstration (OOD) queries fail when faced with out- of-demotion queries. |
| Approach: | They propose a query-aware prompting method that elicits the inherent generalizability of large language models by query-based demo generation. |
| Outcome: | The proposed method outperforms state-of-the-art methods in the OOD setting and two public math benchmarks. |
SkillVerse : Assessing and Enhancing LLMs with Tree Evaluation (2025.acl-long)
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| Challenge: | Language models evolve to tackle complex, multifaceted tasks, requiring granular evaluations . recent studies have focused on leaderboard and benchmark results, but limited interpretability makes it difficult to compare strengths and weaknesses of models. |
| Approach: | They propose an unsupervised tree-structured diagnosis framework for understanding model proficiency in specific abilities with an LLM as a judge. |
| Outcome: | The proposed framework improves model in-context learning and predicts model weaknesses with a 55% success rate compared to the framework without SkillVerse. |
Traffic Light or Light Traffic? Investigating Phrasal Semantics in Large Language Models (2024.findings-emnlp)
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| Challenge: | Phrases are fundamental linguistic units through which humans convey semantics. |
| Approach: | They assess the capacity of API-based large language models to comprehend phrase semantics . they use three human-annotated datasets to analyze their results . |
| Outcome: | The proposed model outperforms embedding-based methods in phrase semantic reasoning tasks . the proposed model does not show significant advantage over fine-tuned methods . |
“You Are An Expert Linguistic Annotator”: Limits of LLMs as Analyzers of Abstract Meaning Representation (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) demonstrate proficiency and fluency in the use of language, but do they have the linguistic knowledge to serve as an expert linguistic annotator? |
| Approach: | They examine the successes and limitations of large language models using the Abstract Meaning Representation (AMR) parsing formalism. |
| Outcome: | The proposed models can reproduce the basic format of AMR, as well as some core event, argument, and modifier structure, but they have virtually no fully accurate parses. |
Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks (2026.acl-long)
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| Challenge: | In-context learning (ICL) enables models to adapt to new tasks via inference-time demonstrations. |
| Approach: | They propose a simple inference-stage enhancement method that reinforces task mapping transfer. |
| Outcome: | The proposed method strengthens task mapping transfer in multimodal models . it performs comparable to text-only ICL in zero-shot settings but degrades significantly under few-shot demonstrations. |
Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning Through Trap Problems (2024.emnlp-main)
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| Challenge: | Current LLMs lack systematic compositionality, and therefore cannot serve as reliable cognitive models. |
| Approach: | They propose to introduce logical traps into the original problems of MATH and GSM8K to investigate the compositionality of large language models in mathematical reasoning. |
| Outcome: | The proposed model can generate infinite combinations from finite learned components. |
LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark (2026.findings-acl)
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Guangyi Liu, Pengxiang Zhao, Liang Liu, Zhiming Chen, Yuxiang Chai, Yaozhen Liang, WenHao Wang, Siheng Chen, Zhengxi Lu, Shuai Ren, Hao Wang, Shibo He, Yong Liu, Wenchao Meng
| Challenge: | Mobile GUI agents show promise in automating tasks but face significant generalization challenges in long-tail scenarios. |
| Approach: | They propose a benchmark framework for mobile GUI agents that measures the performance of GUI agents by analyzing their performance. |
| Outcome: | The LearnGUI benchmark outperforms existing methods in offline and online evaluations and demonstrates consistent gains across model architectures. |
Neuron-Aware Active Few-Shot Learning for LLMs (2026.acl-long)
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| Challenge: | Existing methods rely on output-level signals for sample identification, such as predictive entropy or semantic similarities with test-time data, which overlook models’ internal dynamics which could pinpoint specific knowledge gaps. |
| Approach: | They propose a Neuron-Aware Active Few-Shot Learning framework that shifts the selection paradigm from output-level proxies to models’ internal dynamics. |
| Outcome: | Experiments on three datasets show that NeuFS outperforms existing AFSL baselines. |