ExpNote: Black-box Large Language Models are better Task Solvers with Experience Notebook (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown great power in solving various tasks but fail in many specific tasks. |
| Approach: | They propose a framework to help black-box LLMs better adapt to unfamiliar tasks by reflecting and noting experiences from training data and retrieving them from external memory during testing. |
| Outcome: | The proposed framework improves the performance of black-box Large Language Models on multiple tasks and demonstrates that it is a good choice for the future. |
Similar Papers
An Improved, Strong Baseline for Pre-Trained Large Language Models as Task-Oriented Dialogue Systems (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have shown that Large Language Models perform insufficiently as TOD systems. |
| Approach: | They propose a self-checking mechanism to improve LLM performance as TOD systems. |
| Outcome: | The proposed model outperforms existing models and improves their performance. |
ExpeTrans: LLMs Are Experiential Transfer Learners (2025.acl-long)
Copied to clipboard
| Challenge: | Recent studies provide large language models with textual task-solving experiences via prompts to improve their performance. |
| Approach: | They propose to use prompts to provide LLMs with textual task-solving experiences during their inference stage. |
| Outcome: | The proposed framework improves the performance of large language models on 13 datasets. |
CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Methods for adapting language models to new tasks and domains have traditionally assumed white-box access to the model and work by modifying its parameters. |
| Approach: | They propose a method for adapting large language models to new domains and tasks . they fine-tune a small white-box LM and combine it with a large black-box model at the probability level through a network, learned on a smaller validation set. |
| Outcome: | The proposed method improves performance in all cases, while using a domain expert 23x smaller. |
Challenging Large Language Models with New Tasks: A Study on their Adaptability and Robustness (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluation approaches for large language models (LLMs) rely on existing tasks and benchmarks, raising concerns about test set contamination and the genuine comprehension abilities of LLMs. |
| Approach: | They propose to evaluate LLMs by designing new tasks, automatically generating evaluation datasets for the tasks, and conducting detailed error analyses to scrutinize LLM's adaptability to new tasks. |
| Outcome: | The proposed method examines LLMs’ adaptability to new tasks, their sensitivity to prompt variations, and their error tendencies. |
Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent studies focus on performance benchmarks without fully comparing LLMs to graph learning models. |
| Approach: | They evaluate off-the-shelf and instruction-tuned graph learning models across a variety of scenarios. |
| Outcome: | The proposed models outperform traditional graph learning models in few-shot settings, the authors show . their models out perform models with instruction tuning, and they show excellent generalization and robustness. |
DPDLLM: A Black-box Framework for Detecting Pre-training Data from Large Language Models (2024.findings-acl)
Copied to clipboard
Baohang Zhou, Zezhong Wang, Lingzhi Wang, Hongru Wang, Ying Zhang, Kehui Song, Xuhui Sui, Kam-Fai Wong
| Challenge: | Existing methods to detect pretraining data from large language models are unrealistic to them. |
| Approach: | They propose to detect pre-training data from LLM in a black-box way by using GPT-2 as reference model and feed it with sequence probabilities to detect whether it was used to train it. |
| Outcome: | The proposed framework outperforms existing methods on the benchmark datasets and shows that it is effective on different popular LLMs. |
Adaptation of Large Language Models (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | a tutorial on adaptation of large language models addresses the growing demand for models that go beyond static capabilities. |
| Approach: | This tutorial will provide an overview of dynamic, domain-specific, and task-adaptive LLM adaptation techniques. |
| Outcome: | This tutorial will outline dynamic, domain-specific, and task-adaptive LLM adaptation techniques. |
Black-Box Prompt Optimization: Aligning Large Language Models without Model Training (2024.acl-long)
Copied to clipboard
| Challenge: | Large language models are often not well aligned with human intents, which requires additional training. |
| Approach: | They propose to use Black-Box Prompt Optimization (BPO) to perform alignments on large language models that are not well aligned with human intents. |
| Outcome: | The proposed model outperforms existing models and is model-agnostic. |
ARL2: Aligning Retrievers with Black-box Large Language Models via Self-guided Adaptive Relevance Labeling (2024.acl-long)
Copied to clipboard
| Challenge: | Existing retrievers are misaligned with large language models due to separate training processes and inherent black-box nature of LLMs. |
| Approach: | They propose a retriever learning technique that harnesses LLMs as labelers to annotate and score adaptive relevance evidence. |
| Outcome: | Extensive experiments show that ARL2 improves accuracy and reduces the cost of API calls. |
Current Advances in LLM Reasoning (2026.acl-tutorials)
Copied to clipboard
| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
| Outcome: | This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning. |