2/7/2024 0 Comments Stuck in quicksandInstead, it’s reduced to a grim and serious marriage metaphor plot device. Nor does the jungle element come through as much as it could. The hunters and Las Arenas never get explored beyond setting up the plot, even as a side story incorporates them. On the other hand, Quicksand never once acknowledges how outlandish this scenario can be. On the one hand, Beltrán’s commitment to taking the couple’s predicament seriously results in compelling set pieces and a tender approach to characters that ultimately endear. It results in a feature at odds with itself. Josh and Sofia ultimately encounter too few obstacles, each getting briskly swept aside to give precedence to deep-seated relationship issues. But the marriage woes take up too much space in this underplotted thriller, leading to a repetitive, superficial pattern. On that front, Hawco’s disarming empathy and Gaitán’s relatable resentment of forced pragmatism lend realism and rooting interest. Each jungle obstacle peels back layers to Jack and Sofia’s history, humanizing the initially icy Sofia and revealing a fuller picture that led to their crumbling relationship. Once the spouses get firmly lodged in place, enmeshed in unforgiving mother nature, the plotting slows to a crawl as the script relies on scant few encounters with slithering animals to submerse Josh and Sofia in a primal marriage counseling of sorts. The grueling exhaustion from the thick muck holding them in place, threatening to pull them under completely, is effective thanks to the tangible component. Josh and Sofia get sucked into more of a boggy mud pit that makes their physical performances palpable. The icy tension between the pair only heightens the punishment mother nature has in store for them.Ĭarolina Gaitan as Sofia and Allan Hawco as Josh in Quicksand – Photo Credit: Manuel Olarte/Shudderīeltrán takes a tactile, practical approach to the setting. A series of events sees the pair venturing unwittingly out to Las Arenas, where they wind up trapped in quicksand with no way out and no one aware of their location. It’s exacerbated by Josh’s eagerness to take a hiking excursion while the more rigid Sofia longs to get business done and return home to the kids. The estranged couple is gearing up for divorce, making this business trip even more uncomfortable. Cut to couple Josh ( Allan Hawco ) and his wife Sofia ( Carolina Gaitán ) as they head to a medical conference in Bogotá but the car ride there is rife with friction despite Josh’s best efforts to diffuse the tension. It leaves the thrills underplotted in Quicksand, however.Īn opening sequence introduces the unforgiving terrain a pair of poachers prowl a dangerous area of the Colombian jungle called Las Arenas to hunt and retrieve valuable skin from a venomous snake. Director Andrés Beltrán and writer Matt Pitts approach the B-movie concept with a seriousness that nearly works for their efficient thriller, placing relevance on the emotional stakes for its leads over jungle thrills. Our dataset will be made publicly available at this https URL.Shudder survival thriller Quicksand builds an entire premise around its namesake, leaving a bickering, estranged married couple trapped in a pit of despair. This research not only identifies multiple gaps in the capabilities of current models, but also highlights multiple potential directions for future development. This strongly suggests that current LLMs lack robust mathematical skills and deep reasoning abilities. The results show a significant performance drop across all the models against the perturbed questions. We conducted comprehensive evaluation of both closed-source and open-source LLMs on MORE. This process was guided by our ontology and involved a thorough automatic and manual filtering process, yielding a set of 216 maths problems. Using GPT-4, we generated the MORE dataset by perturbing randomly selected five seed questions from GSM8K. These controlled perturbations span across multiple fine dimensions of the structural and representational aspects of maths questions. In response, we develop (i) an ontology of perturbations of maths questions, (ii) a semi-automatic method of perturbation, and (iii) a dataset of perturbed maths questions to probe the limits of LLM capabilities in mathematical reasoning tasks. However, the true depth of their competencies and robustness, in mathematical reasoning tasks, remains an open question. Download a PDF of the paper titled Stuck in the Quicksand of Numeracy, Far from AGI Summit: Evaluating LLMs' Mathematical Competency through Ontology-guided Perturbations, by Pengfei Hong and 5 other authors Download PDF HTML (experimental) Abstract:Recent advancements in Large Language Models (LLMs) have showcased striking results on existing logical reasoning benchmarks, with some models even surpassing human performance.
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