Zhemg Lee

The advent of in-context learning (ICL) allows pretrained large language models (LLMs) to handle unseen inputs by leveraging context, without requiring parameter updates. However, the success of ICL is strongly influenced by factors such as the quality, size, and ordering of demonstrations, often resulting in unstable or less-than-ideal outcomes. This work is the first to address these limitations through the lens of demonstration augmentation. We first propose a simple yet effective ICL method, termed implicit demonstration augmentation-based ICL (IDAICL), that enriches demonstrations by leveraging their deep feature distributions, integrating knowledge from the entire demonstration set to enhance LLM predictions. From a theoretical standpoint, we demonstrate that when the number of augmented samples tends to infinity, our method asymptotically converges to a new form of logit calibration. Building upon this foundation, we further propose a domain-aware IDAICL (D-IDAICL) method, which improves the precision of knowledge integration by identifying and leveraging the most pertinent domain for each test sample during augmentation. Specifically, a hypernetwork is employed to adaptively select the most effective domain based on the deep representation of the test sample. The corresponding domain-specific knowledge is then utilized to augment the demonstrations, resulting in a domain-aware logit calibration function that enhances predictive performance. Comprehensive evaluations across multiple tasks using eight LLMs reveal that both approaches markedly boost overall and worst-case accuracy, leading to improved robustness and predictive capability. In addition, our approaches mitigate performance fluctuations across different demonstrations, orderings, and templates, while also showing effectiveness in handling class imbalance.

In-context learning (ICL) empowers large pre-trained language models (PLMs) to predict outcomes for unseen inputs without parameter updates. However, the efficacy of ICL heavily relies on the choice of demonstration examples. Randomly selecting from the training set frequently leads to inconsistent performance. Addressing this challenge, this study takes a novel approach by focusing on training data valuation through causal inference. Specifically, we introduce the concept of average marginal effect (AME) to quantify the contribution of individual training samples to ICL performance, encompassing both its generalization and robustness. Drawing inspiration from multiple treatment effects and randomized experiments, we initially sample diverse training subsets to construct prompts and evaluate the ICL performance based on these prompts. Subsequently, we employ Elastic Net regression to collectively estimate the AME values for all training data, considering subset compositions and inference performance. Ultimately, we prioritize samples with the highest values to prompt the inference of the test data. Across various tasks and with seven PLMs ranging in size from 0.8B to 33B, our approach consistently achieves state-of-the-art performance. Particularly, it outperforms Vanilla ICL and the best-performing baseline by an average of 14.1% and 5.2%, respectively. Moreover, prioritizing the most valuable samples for prompting leads to a significant enhancement in performance stability and robustness across various learning scenarios. Impressively, the valuable samples exhibit transferability across diverse PLMs and generalize well to out-of-distribution tasks.

Optical packet header recognition is an important signal processing task of optical communication networks. In this work, we propose an all-optical reservoir, consisting of integrated double-ring resonators (DRRs) as nodes, for fast and accurate optical packet header recognition. As the delay-bandwidth product (DBP) of the node is a key figure-of-merit in the reservoir, we adopt a deep reinforcement learning algorithm to maximize the DBPs for various types of DRRs, which has the advantage of full parameter space optimization and fast convergence speed. Intriguingly, the optimized DBPs of the DRRs in cascaded, parallel, and embedded configurations reach the same maximum value, which is believed to be the global maximum. Finally, 3-bit and 6-bit packet header recognition tasks are performed with the all-optical reservoir consisting of the optimized cascaded rings, which have greatly reduced chip size and the desired “flat-top” delay spectra. Using this optical computing scheme, word-error rates as low as 5×10 ^-4 and 9×10 ^-4 are achieved for 3-bit and 6-bit packet header recognition tasks, respectively, which are one order of magnitude better than the previously reported values.