Modular and composable transfer learning is an emerging direction in the field of Parameter Efficient Fine-Tuning, as it enables neural networks to better organize various aspects of knowledge, leading to improved cross-task generalization. In this paper, we introduce a novel approach Customized Polytropon ($\texttt{C-Poly}$) that combines task-common skills and task-specific skills, while the skill parameters being highly parameterized using low-rank techniques. Each task is associated with a customizable number of exclusive specialized skills and also benefits from skills shared with peer tasks. A skill assignment matrix is jointly learned. To evaluate our approach, we conducted extensive experiments on the Super-NaturalInstructions and the SuperGLUE benchmarks. Our findings demonstrate that $\texttt{C-Poly}$ outperforms fully-shared, task-specific, and skill-indistinguishable baselines, significantly enhancing the sample efficiency in multi-task learning scenarios.

Retrieval Augmented Generation (RAG) has become prevalent in question-answering (QA) tasks due to its ability of utilizing search engine to enhance the quality of long-form question-answering (LFQA). Despite the emergence of various open source methods and web-enhanced commercial systems such as Bing Chat, two critical problems remain unsolved, i.e., the lack of factuality and clear logic in the generated long-form answers. In this paper, we remedy these issues via a systematic study on answer generation in web-enhanced LFQA. Specifically, we first propose a novel outline-enhanced generator to achieve clear logic in the generation of multifaceted answers and construct two datasets accordingly. Then we propose a factuality optimization method based on a carefully designed doubly fine-grained RLHF framework, which contains automatic evaluation and reward modeling in different levels of granularity. Our generic framework comprises conventional fine-grained RLHF methods as special cases. Extensive experiments verify the superiority of our proposed Factuality-optimized RAG (FoRAG) method on both English and Chinese benchmarks. In particular, when applying our method to Llama2-7B-chat, the derived model FoRAG-L-7B outperforms WebGPT-175B in terms of three commonly used metrics (i.e., coherence, helpfulness, and factuality), while the number of parameters is much smaller (only 1/24 of that of WebGPT-175B). Our datasets and models are made publicly available for better reproducibility.https://huggingface.co/forag łabelfootnote_dataset_url

The prevailing issue of factual inconsistency errors in conventional Retrieval Augmented Generation (RAG) motivates the study of Factual Consistency Evaluation (FCE). Despite the various FCE methods proposed earlier, these methods are evaluated on datasets generated by specific Large Language Models (LLMs). Without a comprehensive benchmark, it remains unexplored how these FCE methods perform on other LLMs with different error distributions or even unseen error types, as these methods may fail to detect the error types generated by other LLMs. To fill this gap, in this paper, we propose the first comprehensive FCE benchmark Face4RAG for RAG independent of the underlying LLM. Our benchmark consists of a synthetic dataset built upon a carefully designed typology for factuality inconsistency error and a real-world dataset constructed from six commonly used LLMs, enabling evaluation of FCE methods on specific error types or real-world error distributions. On the proposed benchmark, we discover the failure of existing FCE methods to detect the logical fallacy, which refers to a mismatch of logic structures between the answer and the retrieved reference. To fix this issue, we further propose a new method called L-Face4RAG with two novel designs of logic-preserving answer decomposition and fact-logic FCE. Extensive experiments show L-Face4RAG substantially outperforms previous methods for factual inconsistency detection on a wide range of tasks, notably beyond the RAG task from which it is originally motivated. Both the benchmark and our proposed method are publicly available. https://huggingface.co/datasets/yq27/Face4RAG

This paper investigates the Joint Optimization of Base station sleeping, Functional split, and Routing selection (JOBFR) in virtualized radio access network (vRAN) architectures to minimize the operator’s total cost, including operating cost and migration cost while satisfying user requirements. Specifically, we first use mathematical methods to describe the relationships between the base station (BS) sleeping, functional split (FS) and routing selection, and then model the goal as a joint optimization problem. Next, we propose a heuristic algorithm called Flexible Sleeping, Functional split, and Routing selection (FSFR) to decide BS sleeping, and FS option, and routing selection, simultaneously. Finally, we perform extensive simulations to evaluate our algorithm. The results demonstrate that our algorithm can significantly save the total cost of operators compared to the baseline approach and can also achieve near-optimal performance.