Post-training quantization reduces the computational demand of Large Language Models (LLMs) but can weaken some of their capabilities. Since LLM abilities emerge with scale, smaller LLMs are more sensitive to quantization. In this paper, we explore how quantization affects smaller LLMs’ ability to perform retrieval-augmented generation (RAG), specifically in longer contexts. We chose personalization for evaluation because it is a challenging domain to perform using RAG as it requires long-context reasoning over multiple documents. We compare the original FP16 and the quantized INT4 performance of multiple 7B and 8B LLMs on two tasks while progressively increasing the number of retrieved documents to test how quantized models fare against longer contexts. To better understand the effect of retrieval, we evaluate three retrieval models in our experiments. Our findings reveal that if a 7B LLM performs the task well, quantization does not impair its performance and long-context reasoning capabilities. We conclude that it is possible to utilize RAG with quantized smaller LLMs.
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We examined the effects of age on automatic and voluntary motor adjustments in pointing tasks. To this end, young (20–25 years) and middle-aged adults (48–62 years) were instructed to point at a target that could unexpectedly change its location (to the left or right) or its color (to green or red) during the movement. In the location change conditions, participants were asked to either adjust their pointing movement toward the new location (i.e., normal pointing) or in the opposite direction (i.e., anti-pointing). In the color change conditions, participants were instructed to adjust their movement to the left or right depending on the change in color. The results showed that in a large proportion of the anti-pointing trials, participants made two adjustments: an early initial automatic adjustment in the direction of the target shift followed by a late voluntary adjustment toward the opposite direction. It was found that the late voluntary adjustments were delayed for the middle-aged participants relative to the young participants. There were no age differences for the fast automatic adjustment in normal pointing, but the early adjustment in anti-pointing tended to be later in the middle-aged adults. Finally, the difference in the onset of early and late adjustments in anti-pointing adjustments was greater among the middle-aged adults. Hence, this study is the first to show that aging slows down voluntary goal-directed movement control processes to greater extent than the automatic stimulus-driven processes.
Research into automatic text simplification aims to promote access to information for all members of society. To facilitate generalizability, simplification research often abstracts away from specific use cases, and targets a prototypical reader and an underspecified content creator. In this paper, we consider a real-world use case – simplification technology for use in Dutch municipalities – and identify the needs of the content creators and the target audiences in this scenario. The stakeholders envision a system that (a) assists the human writer without taking over the task; (b) provides diverse outputs, tailored for specific target audiences; and (c) explains the suggestions that it outputs. These requirements call for technology that is characterized by modularity, explainability, and variability. We argue that these are important research directions that require further exploration
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